1 Modelling of Parallel Processing Tasks by Combinatorial Designs

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多层次logistic回归模型

多层次logistic回归模型英文回答：Logistic regression is a popular statistical model used for binary classification tasks. It is a type of generalized linear model that uses a logistic function to model the probability of a certain event occurring. The model is trained using a dataset with labeled examples, where each example consists of a set of input features and a corresponding binary label.The logistic regression model consists of multiple layers, each containing a set of weights and biases. These weights and biases are learned during the training process, where the model adjusts them to minimize the difference between the predicted probabilities and the true labels. The layers can be thought of as a hierarchy of features, where each layer learns to represent more complex and abstract features based on the input features from the previous layer.In the context of deep learning, logistic regression can be extended to have multiple hidden layers, resulting in a multi-layer logistic regression model. Each hidden layer introduces additional non-linear transformations to the input features, allowing the model to learn more complex representations. This makes the model more powerful and capable of capturing intricate patterns in the data.To train a multi-layer logistic regression model, we typically use a technique called backpropagation. This involves computing the gradient of the loss function with respect to the model parameters and updating the parameters using gradient descent. The backpropagation algorithm efficiently calculates these gradients by propagating the errors from the output layer back to the input layer.Multi-layer logistic regression models have been successfully applied to various domains, such as image classification, natural language processing, and speech recognition. For example, in image classification, a multi-layer logistic regression model can learn to recognizedifferent objects in images by extracting hierarchical features from the pixel values.中文回答：多层次logistic回归模型是一种常用的用于二分类任务的统计模型。

Example-based metonymy recognition for proper nouns

Example-Based Metonymy Recognition for Proper NounsYves PeirsmanQuantitative Lexicology and Variational LinguisticsUniversity of Leuven,Belgiumyves.peirsman@arts.kuleuven.beAbstractMetonymy recognition is generally ap-proached with complex algorithms thatrely heavily on the manual annotation oftraining and test data.This paper will re-lieve this complexity in two ways.First,it will show that the results of the cur-rent learning algorithms can be replicatedby the‘lazy’algorithm of Memory-BasedLearning.This approach simply stores alltraining instances to its memory and clas-siﬁes a test instance by comparing it to alltraining examples.Second,this paper willargue that the number of labelled trainingexamples that is currently used in the lit-erature can be reduced drastically.Thisﬁnding can help relieve the knowledge ac-quisition bottleneck in metonymy recog-nition,and allow the algorithms to be ap-plied on a wider scale.1IntroductionMetonymy is aﬁgure of speech that uses“one en-tity to refer to another that is related to it”(Lakoff and Johnson,1980,p.35).In example(1),for in-stance,China and Taiwan stand for the govern-ments of the respective countries:(1)China has always threatened to use forceif Taiwan declared independence.(BNC) Metonymy resolution is the task of automatically recognizing these words and determining their ref-erent.It is therefore generally split up into two phases:metonymy recognition and metonymy in-terpretation(Fass,1997).The earliest approaches to metonymy recogni-tion identify a word as metonymical when it vio-lates selectional restrictions(Pustejovsky,1995).Indeed,in example(1),China and Taiwan both violate the restriction that threaten and declare require an animate subject,and thus have to be interpreted metonymically.However,it is clear that many metonymies escape this characteriza-tion.Nixon in example(2)does not violate the se-lectional restrictions of the verb to bomb,and yet, it metonymically refers to the army under Nixon’s command.(2)Nixon bombed Hanoi.This example shows that metonymy recognition should not be based on rigid rules,but rather on statistical information about the semantic and grammatical context in which the target word oc-curs.This statistical dependency between the read-ing of a word and its grammatical and seman-tic context was investigated by Markert and Nis-sim(2002a)and Nissim and Markert(2003; 2005).The key to their approach was the in-sight that metonymy recognition is basically a sub-problem of Word Sense Disambiguation(WSD). Possibly metonymical words are polysemous,and they generally belong to one of a number of pre-deﬁned metonymical categories.Hence,like WSD, metonymy recognition boils down to the auto-matic assignment of a sense label to a polysemous word.This insight thus implied that all machine learning approaches to WSD can also be applied to metonymy recognition.There are,however,two differences between metonymy recognition and WSD.First,theo-retically speaking,the set of possible readings of a metonymical word is open-ended(Nunberg, 1978).In practice,however,metonymies tend to stick to a small number of patterns,and their la-bels can thus be deﬁned a priori.Second,classic 71WSD algorithms take training instances of one par-ticular word as their input and then disambiguate test instances of the same word.By contrast,since all words of the same semantic class may undergo the same metonymical shifts,metonymy recogni-tion systems can be built for an entire semantic class instead of one particular word(Markert and Nissim,2002a).To this goal,Markert and Nissim extracted from the BNC a corpus of possibly metonymical words from two categories:country names (Markert and Nissim,2002b)and organization names(Nissim and Markert,2005).All these words were annotated with a semantic label —either literal or the metonymical cate-gory they belonged to.For the country names, Markert and Nissim distinguished between place-for-people,place-for-event and place-for-product.For the organi-zation names,the most frequent metonymies are organization-for-members and organization-for-product.In addition, Markert and Nissim used a label mixed for examples that had two readings,and othermet for examples that did not belong to any of the pre-deﬁned metonymical patterns.For both categories,the results were promis-ing.The best algorithms returned an accuracy of 87%for the countries and of76%for the orga-nizations.Grammatical features,which gave the function of a possibly metonymical word and its head,proved indispensable for the accurate recog-nition of metonymies,but led to extremely low recall values,due to data sparseness.Therefore Nissim and Markert(2003)developed an algo-rithm that also relied on semantic information,and tested it on the mixed country data.This algo-rithm used Dekang Lin’s(1998)thesaurus of se-mantically similar words in order to search the training data for instances whose head was sim-ilar,and not just identical,to the test instances. Nissim and Markert(2003)showed that a combi-nation of semantic and grammatical information gave the most promising results(87%). However,Nissim and Markert’s(2003)ap-proach has two major disadvantages.Theﬁrst of these is its complexity:the best-performing al-gorithm requires smoothing,backing-off to gram-matical roles,iterative searches through clusters of semantically similar words,etc.In section2,I will therefore investigate if a metonymy recognition al-gorithm needs to be that computationally demand-ing.In particular,I will try and replicate Nissim and Markert’s results with the‘lazy’algorithm of Memory-Based Learning.The second disadvantage of Nissim and Mark-ert’s(2003)algorithms is their supervised nature. Because they rely so heavily on the manual an-notation of training and test data,an extension of the classiﬁers to more metonymical patterns is ex-tremely problematic.Yet,such an extension is es-sential for many tasks throughout theﬁeld of Nat-ural Language Processing,particularly Machine Translation.This knowledge acquisition bottle-neck is a well-known problem in NLP,and many approaches have been developed to address it.One of these is active learning,or sample selection,a strategy that makes it possible to selectively an-notate those examples that are most helpful to the classiﬁer.It has previously been applied to NLP tasks such as parsing(Hwa,2002;Osborne and Baldridge,2004)and Word Sense Disambiguation (Fujii et al.,1998).In section3,I will introduce active learning into theﬁeld of metonymy recog-nition.2Example-based metonymy recognition As I have argued,Nissim and Markert’s(2003) approach to metonymy recognition is quite com-plex.I therefore wanted to see if this complexity can be dispensed with,and if it can be replaced with the much more simple algorithm of Memory-Based Learning.The advantages of Memory-Based Learning(MBL),which is implemented in the T i MBL classiﬁer(Daelemans et al.,2004)1,are twofold.First,it is based on a plausible psycho-logical hypothesis of human learning.It holds that people interpret new examples of a phenom-enon by comparing them to“stored representa-tions of earlier experiences”(Daelemans et al., 2004,p.19).This contrasts to many other classi-ﬁcation algorithms,such as Naive Bayes,whose psychological validity is an object of heavy de-bate.Second,as a result of this learning hypothe-sis,an MBL classiﬁer such as T i MBL eschews the formulation of complex rules or the computation of probabilities during its training phase.Instead it stores all training vectors to its memory,together with their labels.In the test phase,it computes the distance between the test vector and all these train-ing vectors,and simply returns the most frequentlabel of the most similar training examples.One of the most important challenges inMemory-Based Learning is adapting the algorithmto one’s data.This includesﬁnding a represen-tative seed set as well as determining the rightdistance measures.For my purposes,however, T i MBL’s default settings proved more than satis-factory.T i MBL implements the IB1and IB2algo-rithms that were presented in Aha et al.(1991),butadds a broad choice of distance measures.Its de-fault implementation of the IB1algorithm,whichis called IB1-IG in full(Daelemans and Van denBosch,1992),proved most successful in my ex-periments.It computes the distance between twovectors X and Y by adding up the weighted dis-tancesδbetween their corresponding feature val-ues x i and y i:∆(X,Y)=ni=1w iδ(x i,y i)(3)The most important element in this equation is theweight that is given to each feature.In IB1-IG,features are weighted by their Gain Ratio(equa-tion4),the division of the feature’s InformationGain by its split rmation Gain,the nu-merator in equation(4),“measures how much in-formation it[feature i]contributes to our knowl-edge of the correct class label[...]by comput-ing the difference in uncertainty(i.e.entropy)be-tween the situations without and with knowledgeof the value of that feature”(Daelemans et al.,2004,p.20).In order not“to overestimate the rel-evance of features with large numbers of values”(Daelemans et al.,2004,p.21),this InformationGain is then divided by the split info,the entropyof the feature values(equation5).In the followingequations,C is the set of class labels,H(C)is theentropy of that set,and V i is the set of values forfeature i.w i=H(C)− v∈V i P(v)×H(C|v)2This data is publicly available and can be downloadedfrom /mnissim/mascara.73P F86.6%49.5%N&M81.4%62.7%Table1:Results for the mixed country data.T i MBL:my T i MBL resultsN&M:Nissim and Markert’s(2003)results simple learning phase,T i MBL is able to replicate the results from Nissim and Markert(2003;2005). As table1shows,accuracy for the mixed coun-try data is almost identical to Nissim and Mark-ert’sﬁgure,and precision,recall and F-score for the metonymical class lie only slightly lower.3 T i MBL’s results for the Hungary data were simi-lar,and equally comparable to Markert and Nis-sim’s(Katja Markert,personal communication). Note,moreover,that these results were reached with grammatical information only,whereas Nis-sim and Markert’s(2003)algorithm relied on se-mantics as well.Next,table2indicates that T i MBL’s accuracy for the mixed organization data lies about1.5%be-low Nissim and Markert’s(2005)ﬁgure.This re-sult should be treated with caution,however.First, Nissim and Markert’s available organization data had not yet been annotated for grammatical fea-tures,and my annotation may slightly differ from theirs.Second,Nissim and Markert used several feature vectors for instances with more than one grammatical role andﬁltered all mixed instances from the training set.A test instance was treated as mixed only when its several feature vectors were classiﬁed differently.My experiments,in contrast, were similar to those for the location data,in that each instance corresponded to one vector.Hence, the slightly lower performance of T i MBL is prob-ably due to differences between the two experi-ments.Theseﬁrst experiments thus demonstrate that Memory-Based Learning can give state-of-the-art performance in metonymy recognition.In this re-spect,it is important to stress that the results for the country data were reached without any se-mantic information,whereas Nissim and Mark-ert’s(2003)algorithm used Dekang Lin’s(1998) clusters of semantically similar words in order to deal with data sparseness.This fact,togetherAcc RT i MBL78.65%65.10%76.0%—Figure1:Accuracy learning curves for the mixed country data with and without semantic informa-tion.in more detail.4Asﬁgure1indicates,with re-spect to overall accuracy,semantic features have a negative inﬂuence:the learning curve with both features climbs much more slowly than that with only grammatical features.Hence,contrary to my expectations,grammatical features seem to allow a better generalization from a limited number of training instances.With respect to the F-score on the metonymical category inﬁgure2,the differ-ences are much less outspoken.Both features give similar learning curves,but semantic features lead to a higherﬁnal F-score.In particular,the use of semantic features results in a lower precisionﬁg-ure,but a higher recall score.Semantic features thus cause the classiﬁer to slightly overgeneralize from the metonymic training examples.There are two possible reasons for this inabil-ity of semantic information to improve the clas-siﬁer’s performance.First,WordNet’s synsets do not always map well to one of our semantic la-bels:many are rather broad and allow for several readings of the target word,while others are too speciﬁc to make generalization possible.Second, there is the predominance of prepositional phrases in our data.With their closed set of heads,the number of examples that beneﬁts from semantic information about its head is actually rather small. Nevertheless,myﬁrst round of experiments has indicated that Memory-Based Learning is a sim-ple but robust approach to metonymy recogni-tion.It is able to replace current approaches that need smoothing or iterative searches through a the-saurus,with a simple,distance-based algorithm.Figure3:Accuracy learning curves for the coun-try data with random and maximum-distance se-lection of training examples.over all possible labels.The algorithm then picks those instances with the lowest conﬁdence,since these will contain valuable information about the training set(and hopefully also the test set)that is still unknown to the system.One problem with Memory-Based Learning al-gorithms is that they do not directly output prob-abilities.Since they are example-based,they can only give the distances between the unlabelled in-stance and all labelled training instances.Never-theless,these distances can be used as a measure of certainty,too:we can assume that the system is most certain about the classiﬁcation of test in-stances that lie very close to one or more of its training instances,and less certain about those that are further away.Therefore the selection function that minimizes the probability of the most likely label can intuitively be replaced by one that max-imizes the distance from the labelled training in-stances.However,ﬁgure3shows that for the mixed country instances,this function is not an option. Both learning curves give the results of an algo-rithm that starts withﬁfty random instances,and then iteratively adds ten new training instances to this initial seed set.The algorithm behind the solid curve chooses these instances randomly,whereas the one behind the dotted line selects those that are most distant from the labelled training exam-ples.In theﬁrst half of the learning process,both functions are equally successful;in the second the distance-based function performs better,but only slightly so.There are two reasons for this bad initial per-formance of the active learning function.First,it is not able to distinguish between informativeandFigure4:Accuracy learning curves for the coun-try data with random and maximum/minimum-distance selection of training examples. unusual training instances.This is because a large distance from the seed set simply means that the particular instance’s feature values are relatively unknown.This does not necessarily imply that the instance is informative to the classiﬁer,how-ever.After all,it may be so unusual and so badly representative of the training(and test)set that the algorithm had better exclude it—something that is impossible on the basis of distances only.This bias towards outliers is a well-known disadvantage of many simple active learning algorithms.A sec-ond type of bias is due to the fact that the data has been annotated with a few features only.More par-ticularly,the present algorithm will keep adding instances whose head is not yet represented in the training set.This entails that it will put off adding instances whose function is pp,simply because other functions(subj,gen,...)have a wider variety in heads.Again,the result is a labelled set that is not very representative of the entire training set.There are,however,a few easy ways to increase the number of prototypical examples in the train-ing set.In a second run of experiments,I used an active learning function that added not only those instances that were most distant from the labelled training set,but also those that were closest to it. After a few test runs,I decided to add six distant and four close instances on each iteration.Figure4 shows that such a function is indeed fairly success-ful.Because it builds a labelled training set that is more representative of the test set,this algorithm clearly reduces the number of annotated instances that is needed to reach a given performance.Despite its success,this function is obviously not yet a sophisticated way of selecting good train-76Figure5:Accuracy learning curves for the organi-zation data with random and distance-based(AL) selection of training examples with a random seed set.ing examples.The selection of the initial seed set in particular can be improved upon:ideally,this seed set should take into account the overall dis-tribution of the training examples.Currently,the seeds are chosen randomly.Thisﬂaw in the al-gorithm becomes clear if it is applied to another data set:ﬁgure5shows that it does not outper-form random selection on the organization data, for instance.As I suggested,the selection of prototypical or representative instances as seeds can be used to make the present algorithm more robust.Again,it is possible to use distance measures to do this:be-fore the selection of seed instances,the algorithm can calculate for each unlabelled instance its dis-tance from each of the other unlabelled instances. In this way,it can build a prototypical seed set by selecting those instances with the smallest dis-tance on average.Figure6indicates that such an algorithm indeed outperforms random sample se-lection on the mixed organization data.For the calculation of the initial distances,each feature re-ceived the same weight.The algorithm then se-lected50random samples from the‘most proto-typical’half of the training set.5The other settings were the same as above.With the present small number of features,how-ever,such a prototypical seed set is not yet always as advantageous as it could be.A few experiments indicated that it did not lead to better performance on the mixed country data,for instance.However, as soon as a wider variety of features is taken into account(as with the organization data),the advan-pling can help choose those instances that are most helpful to the classiﬁer.A few distance-based al-gorithms were able to drastically reduce the num-ber of training instances that is needed for a given accuracy,both for the country and the organization names.If current metonymy recognition algorithms are to be used in a system that can recognize all pos-sible metonymical patterns across a broad variety of semantic classes,it is crucial that the required number of labelled training examples be reduced. This paper has taken theﬁrst steps along this path and has set out some interesting questions for fu-ture research.This research should include the investigation of new features that can make clas-siﬁers more robust and allow us to measure their conﬁdence more reliably.This conﬁdence mea-surement can then also be used in semi-supervised learning algorithms,for instance,where the clas-siﬁer itself labels the majority of training exam-ples.Only with techniques such as selective sam-pling and semi-supervised learning can the knowl-edge acquisition bottleneck in metonymy recogni-tion be addressed.AcknowledgementsI would like to thank Mirella Lapata,Dirk Geer-aerts and Dirk Speelman for their feedback on this project.I am also very grateful to Katja Markert and Malvina Nissim for their helpful information about their research.ReferencesD.W.Aha, D.Kibler,and M.K.Albert.1991.Instance-based learning algorithms.Machine Learning,6:37–66.W.Daelemans and A.Van den Bosch.1992.Generali-sation performance of backpropagation learning on a syllabiﬁcation task.In M.F.J.Drossaers and A.Ni-jholt,editors,Proceedings of TWLT3:Connection-ism and Natural Language Processing,pages27–37, Enschede,The Netherlands.W.Daelemans,J.Zavrel,K.Van der Sloot,andA.Van den Bosch.2004.TiMBL:Tilburg Memory-Based Learner.Technical report,Induction of Linguistic Knowledge,Computational Linguistics, Tilburg University.D.Fass.1997.Processing Metaphor and Metonymy.Stanford,CA:Ablex.A.Fujii,K.Inui,T.Tokunaga,and H.Tanaka.1998.Selective sampling for example-based wordsense putational Linguistics, 24(4):573–597.R.Hwa.2002.Sample selection for statistical parsing.Computational Linguistics,30(3):253–276.koff and M.Johnson.1980.Metaphors We LiveBy.London:The University of Chicago Press.D.Lin.1998.An information-theoretic definition ofsimilarity.In Proceedings of the International Con-ference on Machine Learning,Madison,USA.K.Markert and M.Nissim.2002a.Metonymy res-olution as a classification task.In Proceedings of the Conference on Empirical Methods in Natural Language Processing(EMNLP2002),Philadelphia, USA.K.Markert and M.Nissim.2002b.Towards a cor-pus annotated for metonymies:the case of location names.In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC2002),Las Palmas,Spain.M.Nissim and K.Markert.2003.Syntactic features and word similarity for supervised metonymy res-olution.In Proceedings of the41st Annual Meet-ing of the Association for Computational Linguistics (ACL-03),Sapporo,Japan.M.Nissim and K.Markert.2005.Learning to buy a Renault and talk to BMW:A supervised approach to conventional metonymy.In H.Bunt,editor,Pro-ceedings of the6th International Workshop on Com-putational Semantics,Tilburg,The Netherlands. G.Nunberg.1978.The Pragmatics of Reference.Ph.D.thesis,City University of New York.M.Osborne and J.Baldridge.2004.Ensemble-based active learning for parse selection.In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics(HLT-NAACL).Boston, USA.J.Pustejovsky.1995.The Generative Lexicon.Cam-bridge,MA:MIT Press.78。

数据分析英语试题及答案

数据分析英语试题及答案一、选择题（每题2分，共10分）1. Which of the following is not a common data type in data analysis?A. NumericalB. CategoricalC. TextualD. Binary2. What is the process of transforming raw data into an understandable format called?A. Data cleaningB. Data transformationC. Data miningD. Data visualization3. In data analysis, what does the term "variance" refer to?A. The average of the data pointsB. The spread of the data points around the meanC. The sum of the data pointsD. The highest value in the data set4. Which statistical measure is used to determine the central tendency of a data set?A. ModeB. MedianC. MeanD. All of the above5. What is the purpose of using a correlation coefficient in data analysis?A. To measure the strength and direction of a linear relationship between two variablesB. To calculate the mean of the data pointsC. To identify outliers in the data setD. To predict future data points二、填空题（每题2分，共10分）6. The process of identifying and correcting (or removing) errors and inconsistencies in data is known as ________.7. A type of data that can be ordered or ranked is called________ data.8. The ________ is a statistical measure that shows the average of a data set.9. A ________ is a graphical representation of data that uses bars to show comparisons among categories.10. When two variables move in opposite directions, the correlation between them is ________.三、简答题（每题5分，共20分）11. Explain the difference between descriptive andinferential statistics.12. What is the significance of a p-value in hypothesis testing?13. Describe the concept of data normalization and its importance in data analysis.14. How can data visualization help in understanding complex data sets?四、计算题（每题10分，共20分）15. Given a data set with the following values: 10, 12, 15, 18, 20, calculate the mean and standard deviation.16. If a data analyst wants to compare the performance of two different marketing campaigns, what type of statistical test might they use and why?五、案例分析题（每题15分，共30分）17. A company wants to analyze the sales data of its products over the last year. What steps should the data analyst take to prepare the data for analysis?18. Discuss the ethical considerations a data analyst should keep in mind when handling sensitive customer data.答案：一、选择题1. D2. B3. B4. D5. A二、填空题6. Data cleaning7. Ordinal8. Mean9. Bar chart10. Negative三、简答题11. Descriptive statistics summarize and describe thefeatures of a data set, while inferential statistics make predictions or inferences about a population based on a sample.12. A p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true. A small p-value suggests that the observed data is unlikely under the null hypothesis, leading to its rejection.13. Data normalization is the process of scaling data to a common scale. It is important because it allows formeaningful comparisons between variables and can improve the performance of certain algorithms.14. Data visualization can help in understanding complex data sets by providing a visual representation of the data, making it easier to identify patterns, trends, and outliers.四、计算题15. Mean = (10 + 12 + 15 + 18 + 20) / 5 = 14, Standard Deviation = √[(Σ(xi - mean)^2) / N] = √[(10 + 4 + 1 + 16 + 36) / 5] = √52 / 5 ≈ 3.816. A t-test or ANOVA might be used to compare the means ofthe two campaigns, as these tests can determine if there is a statistically significant difference between the groups.五、案例分析题17. The data analyst should first clean the data by removing any errors or inconsistencies. Then, they should transformthe data into a suitable format for analysis, such ascreating a time series for monthly sales. They might also normalize the data if necessary and perform exploratory data analysis to identify any patterns or trends.18. A data analyst should ensure the confidentiality andprivacy of customer data, comply with relevant data protection laws, and obtain consent where required. They should also be transparent about how the data will be used and take steps to prevent any potential misuse of the data.。

Parallel Processing &Distributed Systems

Khoa Coâng Ngheä Thoâng Tin – Ñaïi Hoïc Baùch Khoa Tp.HCM
Models of Parallel Computation
PRAM BSP Phase Parallel
Khoa Coâng Ngheä Thoâng Tin – Ñaïi Hoïc Baùch Khoa Tp.HCM
Pipeline, Processor Array, Multiprocessor, Data Flow Computer Flynn Classification:
– SISD, SIMD, MISD, MIMD
Speedup:
– Amdahl, Gustafson
Pipeline Computer
begin spawn (P0, P1,…, Pn/2 -1) for all Pi where 0 ≤ i ≤ n/2 -1 do for j ← 0 to log n – 1 do if i modulo 2j = 0 and 2i+2j < n the A[2i] ← A[2i] + A[2i+2j] endif endfor endfor end
Khoa Coâng Ngheä Thoâng Tin – Ñaïi Hoïc Baùch Khoa Tp.HCM
Conflicts Resolution Schemes (1)
PRAM execution can result in simultaneous access to the same location in shared memory.
Khoa Coâng Ngheä Thoâng Tin – Ñaïi Hoïc Baùch Khoa Tp.HCM

移动机器人闭环检测的视觉字典树金字塔TF_IDF得分匹配方法

localization and mapping, SLAM), 是实现真正自主移动机器人的关键. 闭环 (Loop closure) 检测是 SLAM 的基础问题之一, 如何准确判断机器人当前位置是否位于已经访问过的环境区域, 对减少机器人位姿和地图状态变量的不确定性, 避免错误引入地图冗余变量或重复结构至关重要[1−10] . SLAM 中常用激光、雷达、超声波作为外传感器, 随着机器视觉的发展, 利用视觉传感器采集的环境信息和图像处理技术进行场景识别和地图构建, 已成为 SLAM 的重要技术. 基于视觉传感信息的 SLAM 中, 闭环检测主要分为概率计算方法[1−4] 和图像匹配方法[5−10] . 概率计算方法将闭环检测归结为递归贝叶斯估计问题. 首先采用 BoW (Bag-of-words) 等图像建模方法描述机器人每一位置的场景图像, 估计已获取图像与对应位置的先验概率, 对当前时刻, 计算该新场景图像与已访问位置匹配的后验概率, 概
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37 卷
率大于阈值则提取为闭环. 图像匹配方法将视觉闭环检测归结于序列图像匹配问题, 将当前时刻的图像与已获取的图像序列进行相似性匹配, 相似度高于阈值的匹配图像对应了机器人的闭环位置. 如何准确建立场景外观模型, 成为基于外观的视觉闭环检测的关键. 目前, 图像分类中的 BoW 方法被广泛用于视觉 SLAM 及闭环检测[1−8] . Cummins[1] 为克服视觉单词间的独立性假设, 用 Chow-Liu 算法逼近单词的概率分布, 用树形结构的贝叶斯网络生成场景外观模型. Angeli[3] 采用形状和颜色特征, 增量构造视觉单词本, 用贝叶斯方法实时计算闭环概率. 然而, 众多视觉 SLAM 研究往往直接引用了图像分类中的 BoW 方法, 没有考虑 SLAM 中图像处理不同于图像分类的特殊性, 如 SLAM 中图像具有时间连续性特征、不同场景图像间存在视觉混淆现象 (Perceptual aliasing)[1] 等, 尤其随机器人移动, 图像数目急剧增加, 计算负荷随之增大, 然而 SLAM 中需要机器人移动到每一个位置, 都能作出即时响应决策, 这便对图像处理的准确性和实时性提出了更高要求. 虽然有研究[5, 8] 采用了如 k -d tree 等最近邻搜索算法提高从图像特征到视觉单词的投影效率, 但未能从根本上克服单词本的诸多弊端. 同时传统视觉字典本的平面结构制约了单词的表征能力和计算效率, 影响了在 SLAM 中的应用效果. 本文基于树形结构的快速搜索特性, 建立分层的视觉字典树 (Visual vocabulary tree)[6, 11] , 不仅视觉单词个数不受限制, 也提高了特征投影时最近邻搜索的效率, 以满足闭环检测的实时性需求. Callmer 在文献 [6] 中虽然采用视觉字典树描述场景图像, 在城市场景中取得了良好的闭环检测效果, 但如同其他现有研究, 在用得分加权匹配方法计算相似度过程中, 往往通过比较由图像在树中自上而下投影得到的路径节点构成的描述向量, 或只由叶子节点单词构成的描述向量间的距离来计算图像间相似性. 这种方法依然是一种平面结构的匹配模式, 忽略了图像在树中不同层上投影的不同量化差异, 忽略了树的层次节点单词之间的不同表征能力. 本文针对树形结构的分层量化特点提出金字塔匹配方法, 通过深度搜索将图像特征投影到视觉字典树, 计算树节点的 TF-IDF (Term frequencyinverse document frequency) 熵, 利用树的高层节点单词鲁棒性强、闭环检测召回率高, 低层节点单词表征性能好、图像相似性计算准确、闭环检测准确率高的特点, 提出自下而上逐层计算图像间的相似性增量, 最后建立匹配核函数整合相似性增量, 从而避免了传统单一量化尺度的量化误差, 以及基于树的平面匹配方法忽略不同层次视觉单词不同表征性能的不足和边界特征错误分类的累积问题对闭环检

SESAM Release Note SIMA V4.1.0说明书

SESAM RELEASE NOTESIMASima is a simulation and analysis tool for marine operations and floating systems — from modelling to post-processing of results.Valid from program version 4.1.0SAFER, SMARTER, GREENERSesam Release NoteSimaDate: 19 Apr 2021Valid from Sima version 4.1.0Prepared by DNV GL – Digital SolutionsE-mail sales: *****************© DNV GL AS. All rights reservedThis publication or parts thereof may not be reproduced or transmitted in any form or by any means, including copying or recording, without the prior written consent of DNV GL AS.DOCUMENTATIONInstallation instructionsRequired:•64 bit Windows 7/8/10•4 GB RAM available for SIMA (e.g. 8 GB RAM total in total on the computer)•1 GB free disk space•Updated drivers for graphics cardNote that Windows Server (all versions), Windows XP, Windows Vista, and any 32-bit Windows are not supported.Recommended:•64-bit Windows 10•16 GB RAM•Fast quad core processor (e.g. Intel i7)•High-resolution screen (1920 × 1200 / 1080p)•Graphics card: DirectX 10.1 or 11.X compatible; 512 MB or higher•F ast SSD disk, as large as possible (capacity requirements depends heavily on simulation settings, e.g. 500 GB is a good start)•3-button mouseHigh disk speed is important if running more than 2 simultaneous simulations in parallel. Example: If the user has enough SIMO-licenses and has configured SIMA to run 4 SIMO-calculations in parallel, then the simulations will probably be disk-speed-bound, and not CPU bound (with the above recommended hardware). Note that this is heavily dependent on the simulation parameters, so the result may vary. The default license type should now allow for unlimited parallel runs on one PC, workstation of cluster.Updated Drivers for Graphics CardThe driver of the graphics card should be upgraded to the latest version. This is especially important if you experience problems with the 3D graphics. Note that the version provided by Windows update is not necessarily up to date – download directly from your hardware vendors web-site.Installing graphics drivers may require elevated access privileges. Your IT support staff should be able to help you with this.SIMA should work with at least one graphics-mode (OpenGL, OpenGL2, DirectX 9 or DirectX 11) for all graphics cards that can run Windows 7 or 8. However, graphics cards can contain defects in their lower-level drivers, firmware and/or hardware. SIMA use the software “HOOPS” from the vendor “Tech Soft 3D” to draw 3D-graphics. For advanced users that would like more information on what graphics cards and drivers that does not work with SIMA (and an indication on what probably will work), please see the web page /hoops/hoops-visualize/graphics- cards/ .Before reading the compatibility table you may want to figure out which version of HOOPS SIMAis using. To do this open Help > About > Installation Details, locate the Plug-ins tab and look for the plug-in provider TechSoft 3D (click the Provider column title twice for a more suitable sort order). The version number is listed in the Version column. Also remember that all modes (OpenGL, OpenGL2, DirectX 9, DirextX 11) are available in SIMA.Upgrading from Earlier VersionsAfter upgrading to a newer version of SIMA, your workspaces may also require an update. This will be done automatically as soon as you open a workspace not created with the new version. You may not be able to open this workspace again using an older version of SIMA.Preference settings should normally be retained after upgrading, however you may want to open the preference dialog ( Window > Preferences ) in order to verify this.Verify Correct InstallationTo verify a correct installation of SIMA, perform the following steps:1.Start SIMA (by the shortcut created when installing, or by running the SIMA executable)a.If you are prompted for a valid license, specify a license file or license server. (If you needadvanced information on license options, see “License configuration”).b.SIMA auto-validates upon startup: A successful installation should not display any errorsor warnings when SIMA is started.2.Create a new, empty workspace:a.You will be prompted to Open SIMA Workspace: Create a new workspace by clicking New,select a different folder/filename if you wish, and click Finish.3.Import a SIMO example, run a SIMO simulation, and show 3D graphics:a.Click the menu Help > Examples > SIMO > Heavy lifting operationb.Expand the node Condition in the Navigator in the upper left cornerc.Right-click Initial, and select Run dynamic analysis. After a few seconds, you will see themessage Dynamic calculation done. No errors should occur.d.Right-click HeavyLifting in the Navigator in the upper left corner, and select Open 3DView. 3D-graphics should be displayed, showing a platform and a crane.4.If there were no errors when doing the above steps, then SIMA can be assumed to becorrectly installed.Changing Default Workspace Path ConfigurationWhen creating a new workspace SIMA will normally propose a folder named Workspace_xx where xx is an incrementing number; placed in the users home directory under SIMA Workspaces.The proposed root folder can be changed by creating a file named .simarc and place it in the users home directory or in the application installation directory (next to the SIMA executable). The file must contain a property sima.workspace.root and a value. For example:sima.workspace.root=c:/SIMA Workspaces/A special case is when you want the workspace root folder to be sibling of the SIMA executable. This can be achieved by setting the property as follows:sima.workspace.root=.License ConfigurationSIMA will attempt to automatically use the license files it finds in this order:e path specified in the file “.simarc” if present. See details below.e the path specified in the license wizard.e the system property SIMA_LICENSE_FILE.e the environment variable SIMA_LICENSE_FILE.e all “*.lic” files found in C:/flexlm/ if on Windows.e all “*.lic” files found in the user home directory.If any of the above matches, the search for more license files will not continue. If there are no matches, SIMA will present a license configuration dialog.The license path can consist of several segments separated by an ampersand character. Note that a license segment value does not have to point to a particular file – it could also point to a license server. For example:c:/licenses/sima.lic&1234@my.license.server&@another.license.serverIn this case the path is composed on one absolute reference to a file. F ollowed by the license server at port 1234 and another license server using the default port number.RIFLEX and SIMO LicenseWhen starting SIMO and RI F LEX from SIMA the environment variable MARINTEK_LICENSE_F ILE will be set to the home directory of the user. This means that a license file can be placed in this directory and automatically picked up.Specifying a License pathWhen starting SIMA without a license the dialog below will pop up before the workbench is shown. If you have a license file; you can simply drag an drop it into the dialog and the path to this file will be used. You may also use the browse button if you want to locate the file by means of the file navigator. If you want to use a license server; use the radio button and select License server then continue to fill in the details. The port number is optional. A host must be specified, however. Note that the host name must be in the form of a DNS or IP-address.You can now press Finish or if you want to add more path segments; you can press Next, this will bring up the second page of the license specification wizard. The page will allow you to add and remove licence path segments and rearrange their individual order.Modifying a License PathIf the license path must be modified it can be done using the dialog found in the main menu; Window >Preferences > License. This preference page works the same as the second page of the wizard.Specifying License Path in .simarcThe mechanism described here works much like specifying the environment variable, however it will also lock down the SIMA license configuration pages, thus denying the user the ability to change the license path. This is often the better choice when installing SIMA in an environment where the IT-department handles both installation and license configuration.The license path can be forced by creating a file named .simarc and place it in the users home directory or in the application installation directory (next to sima.exe). The latter is probably the better choice as the file can be owned by the system and the user can be denied write access. The license path must be specified using the sima.license.path key and a path in the F LEXlm Java format. The license path can consist of several segments separated by an ampersand character. For instance:sima.license.path=c:/licenses/sima.lic&1234@my.license.server&@another.license.serverNote that the version of FLEXlm used in SIMA does not support using Windows registry variables. It also requires the path to be entered in the F LEXlm Java format which is different from the normal F LEXlm format. Using this mechanism one can also specify the license path for physics engines such as SIMO and RIF LEX started from SIMA. This is done by specifying the key marintek.license.path followed by the path in normal FLEXlm format. For example:marintek.license.path=c:/licenses/ sima.lic:1234@my.license.server:@another.license.server Viewing License DetailsIf you would like to view license details, such as expiration dates and locations you will find this in the main menu Help > License.New Features - SIMONew Features - RIFLEXNew Features - OtherBUG FIXESFixed bugs - SIMOFixed bugs - RIFLEXFixed bugs - OtherREMAINING KNOWN ISSUESUnresolved Issues - SIMOUnresolved Issues - RIFLEXUnresolved Issues - OtherABOUT DNV GLDriven by our purpose of safeguarding life, property and the environment, DNV GL enables organizations to advance the safety and sustainability of their business. We provide classification and technical assurance along with software and independent expert advisory services to the maritime, oil and gas, and energy industries. We also provide certification services to customers across a wide range of industries. Operating in more than 100 countries, our 16,000 professionals are dedicated to helping our customers make the world safer, smarter and greener. DIGITAL SOLUTIONSDNV GL is a world-leading provider of digital solutions for managing risk and improving safety and asset performance for ships, pipelines, processing plants, offshore structures, electric grids, smart cities and more. Our open industry platform Veracity, cyber security and software solutions support business-critical activities across many industries, including maritime, energy and healthcare.。

parallel model名词解释

parallel model名词解释
嘿，你知道啥是 parallel model 不？parallel model 啊，就好比是一群小伙伴一起干活儿！比如说吧，想象一下有个大工程，就像盖一座超
级大的城堡。

如果只有一个人在那吭哧吭哧地干，那得啥时候才能盖
好呀！但要是有好多人同时动手，有人搬砖，有人砌墙，有人搭屋顶，这效率不就蹭蹭上去啦！这 parallel model 就像是这样，让很多部分同
时进行工作。

它可不是那种单打独斗的模式哦！再打个比方，就像一场接力比赛，每一棒的选手都在自己的赛道上全力奔跑，然后把接力棒顺利地交给
下一个人，大家齐心协力朝着终点冲呀！在科技领域里，parallel model 可是超级厉害的存在呢！
你想想看，要是电脑处理数据的时候，只有一个部分在慢吞吞地算，那得多耽误事儿呀！但有了 parallel model ，就好像给电脑开了好多条“工作通道”，各个部分都能同时处理数据，速度那叫一个快呀！
它真的是让很多复杂的任务变得简单高效了呢！就像你做家务，一
个人又要扫地又要擦桌子又要洗碗，忙得晕头转向。

但如果能找几个
人一起分工合作，那不就轻松多啦！parallel model 就是这样的神奇呀！
总之呢，parallel model 就是一种能让事情变得更快、更高效的模式，它就像是给我们的工作和生活加了一把劲，让一切都变得更顺畅啦！
我觉得这真的是一个超棒的概念呀，你说是不是呢？。

集成梯度特征归属方法-概述说明以及解释

集成梯度特征归属方法-概述说明以及解释1.引言1.1 概述在概述部分，你可以从以下角度来描述集成梯度特征归属方法的背景和重要性：集成梯度特征归属方法是一种用于分析和解释机器学习模型预测结果的技术。

随着机器学习的快速发展和广泛应用，对于模型的解释性需求也越来越高。

传统的机器学习模型通常被认为是“黑盒子”，即无法解释模型做出预测的原因。

这限制了模型在一些关键应用领域的应用，如金融风险评估、医疗诊断和自动驾驶等。

为了解决这个问题，研究人员提出了各种机器学习模型的解释方法，其中集成梯度特征归属方法是一种非常受关注和有效的技术。

集成梯度特征归属方法能够为机器学习模型的预测结果提供可解释的解释，从而揭示模型对于不同特征的关注程度和影响力。

通过分析模型中每个特征的梯度值，可以确定该特征在预测中扮演的角色和贡献度，从而帮助用户理解模型的决策过程。

这对于模型的评估、优化和改进具有重要意义。

集成梯度特征归属方法的应用广泛，不仅适用于传统的机器学习模型，如决策树、支持向量机和逻辑回归等，也可以应用于深度学习模型，如神经网络和卷积神经网络等。

它能够为各种类型的特征，包括数值型特征和类别型特征，提供有益的信息和解释。

本文将对集成梯度特征归属方法的原理、应用优势和未来发展进行详细阐述，旨在为读者提供全面的了解和使用指南。

在接下来的章节中，我们将首先介绍集成梯度特征归属方法的基本原理和算法，然后探讨应用该方法的优势和实际应用场景。

最后，我们将总结该方法的重要性，并展望未来该方法的发展前景。

1.2文章结构文章结构内容应包括以下内容：文章的结构部分主要是对整篇文章的框架进行概述，指导读者在阅读过程中能够清晰地了解文章的组织结构和内容安排。

第一部分是引言，介绍了整篇文章的背景和意义。

其中，1.1小节概述文章所要讨论的主题，简要介绍了集成梯度特征归属方法的基本概念和应用领域。

1.2小节重点在于介绍文章的结构，将列出本文各个部分的标题和内容概要，方便读者快速了解文章的大致内容。

模拟ai英文面试题目及答案

模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。

normalized cuts and image segmentation翻译

规范化切割和图像分割摘要：为解决在视觉上的感知分组的问题，我们提出了一个新的方法。

我们目的是提取图像的总体印象，而不是只集中于局部特征和图像数据的一致性。

我们把图像分割看成一个图形的划分问题，并且提出一个新的分割图形的全球标准,规范化切割。

这一标准衡量了不同组之间的总差异和总相似。

我们发现基于广义特征值问题的一个高效计算技术可以用于优化标准。

我们已经将这种方法应用于静态图像和运动序列，发现结果是令人鼓舞的。

1简介近75年前，韦特海默推出的“格式塔”的方法奠定了感知分组和视觉感知组织的重要性。

我的目的是，分组问题可以通过考虑图（1）所示点的集合而更加明确。

Figure1:H<iw m.3Uiyps?通常人类观察者在这个图中会看到四个对象，一个圆环和内部的一团点以及右侧两个松散的点团。

然而这并不是唯一的分割情况。

有些人认为有三个对象，可以将右侧的两个认为是一个哑铃状的物体。

或者只有两个对象，右侧是一个哑铃状的物体，左侧是一个类似结构的圆形星系。

如果一个人倒行逆施，他可以认为事实上每一个点是一个不同的对象。

这似乎是一个人为的例子，但每一次图像分割都会面临一个相似的问题一将一个图像的区域D划分成子集Di会有许多可能的划分方式（包括极端的将每一个像素认为是一个单独的实体）。

我们怎样挑选“最正确”的呢？我们相信贝叶斯的观点是合适的，即一个人想要在较早的世界知识背景下找到最合理的解释。

当然，困难在于具体说明较早的世界知识一一些低层次的，例如亮度，颜色，质地或运行的一致性，但是关于物体对称或对象模型的中高层次的知识是同等重要的。

这些表明基于低层次线索的图像分割不能够也不应该旨在产生一个完整的最终的正确的分割。

目标应该是利用低层次的亮度，颜色，质地，或运动属性的一致性继续的提出分层分区。

中高层次的知识可以用于确认这些分组或者选择更深的关注。

这种关注可能会导致更进一步的再分割或分组。

关键点是图像分割是从大的图像向下进行，而不是像画家首先标示出主要区域，然后再填充细节。

mlp多层感知机贝叶斯超参数

mlp多层感知机贝叶斯超参数
多层感知机（MLP）是一种基础的神经网络模型，它可以通过引入激活函数来实现非线性映射，从而解决更加复杂的预测问题。

在训练MLP时，超参数的选择对模型的性能有着重要影响。

贝叶斯方法可以用于优化这些超参数，提高模型的泛化能力。

具体来说，MLP的超参数包括但不限于：
1. 层数：MLP由输入层、隐藏层和输出层组成，隐藏层的层数会影响模型的复杂度。

2. 神经元数量：每一层中的神经元数量，也是决定模型复杂度的重要因素。

3. 激活函数：如sigmoid函数，它可以让MLP从线性模型变为非线性模型，增强模型的表达能力。

4. 学习率：影响模型训练过程中权重更新的速度。

5. 批次大小：决定每次训练时输入数据的量，影响模型的收敛速度和稳定性。

6. 迭代次数：模型训练的总轮数，与模型的训练时间和解的质量都有关系。

7. 正则化参数：用于控制模型的复杂度，防止过拟合。

贝叶斯方法在超参数优化中的应用主要包括：
1. 贝叶斯优化：通过构建目标函数的后验分布，不断迭代寻找最优超参数组合。

2. 概率模型：使用概率模型来描述超参数的不确定性，通过采样来探索超参数空间。

3. 自动化模型选择：结合贝叶斯方法和交叉验证，自动选择最佳的超参数组合。

总的来说，在实践中，贝叶斯方法可以帮助我们更有效地选择MLP的超参数，从而提高模型的性能和泛化能力。

通过贝叶斯优化，我们可以在超参数空间中寻找到最优解，减少手动调参的工作量，加快模型的开发周期。

因果推断大语言模型-概述说明以及解释

因果推断大语言模型-概述说明以及解释1.引言1.1 概述概述部分的内容可以涵盖以下几个方面：引言：在当今科技发展的快速推动下，人工智能领域得到了前所未有的发展。

其中，大语言模型作为自然语言处理（NLP）的重要分支，在近年来引起了广泛关注和研究。

大语言模型是基于深度学习技术和海量数据集训练出的能够生成流畅、准确的文本的模型。

近年来，像OpenAI的GPT系列模型以及谷歌的BERT模型就是代表了大语言模型的研究成果之一。

因果推断是指根据已知的条件或因果关系来推理出某种结果或结论。

在自然语言处理领域，因果推断一直是一个关键而具有挑战性的问题。

而大语言模型的出现为因果推断的解决提供了新的思路和方法。

大语言模型通过学习大规模语料库中的关联性和上下文信息，在某种程度上可以实现因果推断。

本文旨在探讨大语言模型在因果推断中的应用和挑战，详细介绍大语言模型的基本原理和训练方法，并探讨大语言模型在因果推断中的优势和局限性。

通过对相关理论和实践的综合分析，进一步探讨如何针对因果推断的需求来优化和改进大语言模型，并提出未来研究的方向和可能的发展趋势。

本文结构：本文首先介绍大语言模型的基本概念，并对其在自然语言处理领域的重要性进行讨论。

接着，详细介绍大语言模型的训练方法和关键技术，并分析其在因果推断中的应用。

然后，剖析大语言模型在因果推断中面临的挑战和限制，并探讨如何进一步提升其在因果推断任务中的性能。

最后，总结本文的主要观点，并对未来的研究方向进行展望。

通过本文的阐述，读者将能够深入了解大语言模型在因果推断中的应用和挑战，掌握大语言模型的基本原理和训练方法，并对未来在该领域的研究方向有一定的了解和思考。

希望本文能为因果推断领域的研究和实践提供一定的参考和指导。

1.2 文章结构本文将按照以下结构展开讨论因果推断大语言模型的相关内容。

首先，在引言部分，将对整篇文章的背景和意义进行概述。

接着，我们将进入正文部分，对因果推断大语言模型的定义、原理以及相关研究进行详细介绍。

Hunglish nyílt statisztikai magyar-angol gépi nyersfordító

Hunglish:ny´ılt statisztikai magyar-angol g´e pinyersford´ıt´oHal´a csy P´e ter ,Kornai Andr´a s ,N´e meth L´a szl´o∗,Rung Andr´a s∗,Szakad´a tIstv´a n∗,Tr´o n Viktor ,Varga D´a niel∗Abstract.A Budapesti M˝u szaki Egyetem M´e dia Oktat´o´e s Ku-tat´o K¨o zpontj´a nak vezet´e s´e vel2004j´u lius´a ban indult Hunglish pro-jekt1egy szabadon felhaszn´a lhat´o,statisztikai g´e pi nyersford´ıt´o t,il-letve ford´ıt´a st´a mogat´o rendszert hoz l´e tre,magyar nyelv˝u sz¨o vegekangolra val´o´a t¨u ltet´e s´e hez.A g´e pi ford´ıt´o tan´ıt´a s´a hoz egy k´e tnyelv˝uillesztett p´a rhuzamos korpuszt hozunk l´e tre.A projekt lez´a r´a sa ut´a nnemcsak a kifejlesztett szoftvereket,hanem a korpuszt´e s az ezalapj´a n´e p´ıtett/jav´ıtott k´e tnyelv˝u magyar–angol sz´o t´a rat is szabadonhozz´a f´e rhet˝o v´e tessz¨u k b´a rki sz´a m´a ra.1Bevezet´e sA glob´a lis szolg´a ltat´o k szemsz¨o g´e b˝o l a helyi nyelv haszn´a lata elengedhetetlen term´e keik´e s szolg´a ltat´a saik´u j piacokra t¨o rt´e n˝o bevezet´e s´e hez´e s elterjeszt´e s´e hez –k¨u l¨o n¨o sen a term´e kle´ır´a sok´e s az inform´a ci´o-szolg´a ltat´a sok k¨o vetelnek´a lland´o ford´ıt´a si munk´a t.A lok´a lis piacok,a nemzeti kult´u r´a k szemsz¨o g´e b˝o l tekintve azonban m´a s¨o sszef¨u gg´e sek v´a lnak fontoss´a!Az inform´a ci´o´a raml´a s´e s az ebb˝o l fakad´o gazdas´a gi el˝o ny¨o k biztos´ıt´a sa´e rdek´e ben els˝o sorban arra van sz¨u ks´e g, hogy a helyben rendelkez´e sre´a ll´o inform´a ci´o glob´a lisan el´e rhet˝o legyen.A mag-yar viszonyokra vet´ıtve teh´a t kulcsfontoss´a g´u nak tartjuk azt,hogy a magyar term´e kek,szolg´a ltat´a sok´e s´a ltal´a ban magyar nyelven el´e rhet˝o inform´a ci´o k min´e l hat´e konyabban´e s min´e l sz´e lesebb k¨o rben v´a lhassanak ismertt´e.Ahhoz,hogy magyar nyelv˝u inform´a ci´o m´a s nyelven is el´e rhet˝o legyen,t¨o m´e rdek ford´ıt´a si munk´a ra van sz¨u ks´e g.Miut´a n az angol nyelv mind a gazdas´a gi´e letben,mind az inform´a ci´o´a raml´a sban k¨o zponti szerepet kap,´u gy gondoljuk,hogy a magyar nyelvb˝o l val´o g´e pi ford´ıt´a s szempontj´a b´o l az angol a kulcsfontoss´a g´u c´e lnyelv.A projekt els˝o dleges c´e lja´ıgy egy magyar-angol nyersford´ıt´o rendszer´e p´ıt´e se.Nem tekintj¨u k c´e lunknak a magas szint˝u,net´a n irodalmi ig´e ny˝u g´e pi ford´ıt´a st.C´e lunk olyan rendszer elk´e sz´ıt´e se,melynek kimenete egynyelv˝u Budapesti M˝u szaki Egyetem M´e dia Oktat´o´e s Kutat´o K¨o zpont,{hp,nemeth, runga,szakadat,daniel}@mokk.bme.huMetaCarta Inc.,andras@International Graduate College,Saarland University and University of Edinburgh, v.tron@1A projekt indul´a s´a t az Informatikai´e s H´ırk¨o zl´e si Miniszt´e rium ITEM2003 p´a ly´a zat´a n elnyert¨o sszeg biztos´ıtja.inform´a ci´o-visszakeres˝o(IV,angolul information retrieval)rendszerek be-menetek´e nt szolg´a lhat.A t¨o bbnyelv˝u IV rendszerek kutat´a sai,k¨u l¨o n¨o sen az Amerikai Szabv´a ny¨u gyi Hivatal(NIST)´a ltal´e vente megrendezett TREC kon-ferencia“keresztnyelvi IV”(cross-language information retrieval)vizsg´a latai vil´a goss´a tett´e k,hogy az IV rendszerek maguk sem k´e pesek aﬁnom´a rnyalatok megk¨u l¨o nb¨o ztet´e s´e re,´e s l´e nyeg´e ben ugyanazt a teljes´ıtm´e nyt ny´u jtj´a k gyeng´e bb min˝o s´e g˝u(pl.besz´e dfelismer´e sb˝o l sz´a rmaz´o,25-30%-ban hib´a s)sz¨o vegeken,mint a hib´a tlan nyelvtan´u,v´a laszt´e kosan meg´ırt anyagokon.Ez annyit jelent,hogy nyersford´ıt´a s bizonyos haszn´a lati helyzetekben ugyanolyan hasznos,mint egy ig´e nyes emberi ford´ıt´a s.A projekt v´e geredm´e nyek´e nt egy m˝u k¨o d˝o k´e pes nyersford´ıt´o szolg´a ltat´a s pro-tot´ıpusa fog elk´e sz¨u lni.A szoftvereket,vagyis a ford´ıt´o program k´o dj´a t´e s a munka sor´a n kifejlesztett eszk¨o zk´e szletet,valamint a fel´e p´ıtett adatb´a zisokat, a k´e tnyelv˝u illesztett korpuszt´e s a k´e tnyelv˝u sz´o t´a rat szabadon hozz´a f´e rhet˝o v´e tessz¨u k.A munka sor´a n kidolgozott m´o dszereket´e s technol´o gi´a t publik´a ci´o k,il-letve haszn´a lati k´e zik¨o nyvek form´a j´a ban kiadjuk.A projekt eredm´e nyeit ez´a ltal b´a rki el´e rheti,felhaszn´a lhatja,illetve tov´a bbfejlesztheti,vagy a technol´o gi´a ra ´e p´ıtve¨o n´a ll´o szolg´a ltat´a st ind´ıthat.Az eredm´e nyekhez val´o szabad hozz´a f´e r´e s a projekt egyik kulcsfontoss´a g´u eleme,amellyel sz´a mos c´e lunk van.Egyr´e szt´ıgy l´a tjuk biztos´ıtva,hogy a t´a mogat´a s megsz˝u n´e s´e vel a fejleszt´e sek tov´a bb folytat´o dhatnak,ak´a r a jelen pro-jekt r´e sztvev˝o it˝o l teljesen f¨u ggetlen¨u l is.M´a sr´e szt,minden olyan kutat´o-´e s fe-jleszt˝o csoport munk´a j´a t t´a mogatni k´ıv´a njuk,amely valamilyen m´o don a magyar nyelvtechnol´o gi´a val foglalkozik.A projekt olyan alapvet˝o fontoss´a g´u technol´o giai megold´a sokat´e s adatforr´a sokat tesz hozz´a f´e rhet˝o v´e,melyek mind tov´a bbi alap-kutat´a sokhoz,mind gyakorlati alkalmaz´a sok fejleszt´e s´e hez elengedhetetlenek. 2A projekt c´e ljaiA g´e pi ford´ıt´a s l´e nyeg´e ben a sz´a m´ıt´o g´e p megjelen´e s´e vel egyid˝o s v´a llalkoz´a s;az els˝o ilyen c´e l´u programot1947-ben fejlesztett´e k ki Weaver´e s munkat´a rsai.A g´e pi ford´ıt´a s neh´e zs´e geit¨o sszegz˝o ALPAC jelent´e s[2]meg´a llap´ıt´a sai sok tekintetben m´a ig´e rv´e nyesek,´e s emiatt nem meglep˝o,hogy a g´e pi ford´ıt´a s alkalmaz´a si k¨o re meglehet˝o sen korl´a tozott.K¨o ztudom´a s´u,hogy a g´e pi ford´ıt´o rendszerek kimenete k´e zi ut´o szerkeszt´e s n´e lk¨u l emberi kommunik´a ci´o ra nem alkalmas,az automatikus ford´ıt´a sok gyakran kifejezetten komikus hat´a st keltenek.´Eppen ez´e rt jelen pro-jekt c´e lja sem az els˝o dlegesen emberi fogyaszt´a sra sz´a nt v´e gleges ford´ıt´a s,hanem csak a g´e pi vagy ut´o szerkeszt˝o i felhaszn´a l´a sra sz´a nt nyersford´ıt´a s.Ehhez a f˝o c´e lhoz vezet˝o munk´a lataink sor´a n a projekt t¨o bb olyan r´e szeredm´e nyt is felmutat majd,amelyek¨o nmagukban is jelent˝o s nyelvtech-nol´o giai hozz´a j´a rul´a sk´e nt tekinthet˝o ek:–magyar-angol sz´o t´a r:szabad felhaszn´a l´a s´u,gyakoris´a gi inform´a ci´o kat is tar-talmaz´o elektronikus magyar-angol sz´o t´a r–a statisztikai alap´u sz´o t´a rak el˝o´a ll´ıt´a s´a hoz,karbantart´a s´a hoz´e s jav´ıt´a s´a hoz sz¨u ks´e ges infrastrukt´u ra–p´a rhuzamos korpusz:szabad felhaszn´a l´a s´u,mondatonk´e nt illesztett magyar-angol p´a rhuzamos sz¨o vegkorpusz–nyersford´ıt´o:szabad forr´a s´u rejtett Markov modell alap´u nyersford´ıt´o tech-nol´o giaA nyersford´ıt´a s legfontosabb eszk¨o ze a k´e tnyelv˝u sz´o t´a r.Imm´a r harminc ´e ve vannak forgalomban olyan ford´ıt´a st´a mogat´o rendszerek,melyek els˝o sorban a szavak sz´o t´a ri kikeres´e s´e nek munk´a j´a t automatiz´a lj´a k.Projekt¨u nk els˝o c´e lja egy jogtiszta,szabadon felhaszn´a lhat´o magyar-angol sz´o t´a r publik´a l´a sa,ame-lyet az egy´e ni felhaszn´a l´o k´e s a szoftverfejleszt˝o k¨o z¨o ss´e g szabadon b˝o v´ıthet tov´a bb.Ehhez komoly hozz´a j´a rul´a s Vony´o Attila k¨o zismert k´e tnyelv˝u g´e pi sz´o t´a ra.Amennyiben a magyarorsz´a gi K+F-t´a mogat´a si rendszer keret´e ben tov´a bbi angol-magyar rendszerek is´e p¨u lnek,´e s amennyiben az alkot´o k hajland´o k ezek sz´o anyag´a t is ny´ılt forr´a sk´o d´u v´a tenni(ide´e rtj¨u k nemcsak a kutat´a si,hanem a kereskedelmi c´e lra val´o tov´a bbfelhaszn´a l´a s korl´a toz´a s n´e lk¨u li enged´e lyez´e s´e t is), annyiban rendszer¨u nk sz´o t´a ra ezekkel tov´a bb b˝o v´ıthet˝o.A sz´o t´a ri ekvivalenci´a n alapul´o(nyers)ford´ıt´a snak ragozott szavak´e s sz´o t´a ri t´e telek probl´e m´a j´a n k´ıv¨u l k´e t alapprobl´e m´a val kell megk¨u zdenie.Az els˝o probl´e ma a c´e lnyelv´e s t´a rgynyelv nyelvtani elt´e r´e sei.Eset¨u nkben ez k¨u l¨o n¨o sen nagy probl´e mak´e nt jelentkezik az angol´e s a magyar nyelvi rendszer jelent˝o s k¨u l¨o nbs´e gei miatt.Amit az angol tipikusan sz´o rendis´e ggel fejez ki(pl.az alany/´a ll´ıtm´a ny/t´a rgy megk¨u l¨o nb¨o ztet´e st)azt a magyar ragokkal´e rz´e kelteti. Miut´a n c´e lunk els˝o sorban a g´e pi IV-t t´a mogat´o nyersford´ıt´a s,a probl´e ma nagy-obb r´e sz´e t–els˝o sorban az angol sz´o rendﬁnoms´a gainak algoritmiz´a l´a s´a t–mi ﬁgyelmen k´ıv¨u l hagyhatjuk,hiszen az inform´a ci´o-visszakeres˝o rendszerek eleve a sz¨o veg sorrendis´e g´e t elhanyagol´o“sz´o zs´a k”(angolul bag of words)modelleken alapulnak.Egy m´a sik probl´e ma a sz´o t´a ri t¨o bb´e rtelm˝u s´e g.P´e ld´a ul a magyar nap sz´o egyszerre jelenti az´e gitestet´e s az id˝o egys´e get,amelyet az angol nyelv k´e t k¨u l¨o n sz´o val fejez ki(sun,illetve day).Miut´a n egy magyar sz´o n´a l´a tlagban h´a rom an-gol ekvivalenssel is lehet sz´a molni,egy h´e tszavas magyar mondat leford´ıt´a sa37 (teh´a t t¨o bb mint k´e tezer)vari´a nst k´ın´a l.Erre a probl´e m´a ra megold´a st ny´u jt a sz¨o vegk¨o rnyezetben tal´a lhat´o inform´a ci´o,p´e ld´a ul abban a kifejez´e sben,hogy ’a nap´e s bolyg´o i’a nap sz´o egy´e rtelm˝u en a sun,m´ıg abban,hogy’egy es˝o s nap’egy´e rtelm˝u en a day ford´ıt´a st kaphatja.Vil´a gos,hogy az ilyen k¨o rnyezett˝o l f¨u gg˝o val´o sz´ın˝u ford´ıt´a sok megtal´a l´a s´a hoz sz¨u ks´e ges,hogy a sz¨o vegk¨o rnyezet ´a ltal ny´u jtott inform´a ci´o t pontosan meg tudjuk ragadni´e s azt elvszer˝u en in-tegr´a ljuk a potenci´a lis ekvivalensek kiv´a laszt´a s´a nak folyamat´a ban.A nyelvi elemek egym´a s k¨o rnyezet´e ben val´o megjelen´e s´e nek statisztikai elm´e let´e t m´e g a m´u lt sz´a zad elej´e n alkotta meg A. A.Markov.Ma en-nek az elm´e letnek k¨u l¨o nf´e le v´a ltozatai l´e teznek:a Markov-l´a ncok(angolul Markov chains)´e s az´u n.rejtett Markov modellek(HMM,angolul Hidden Markov Model)a nyelvtechnol´o gia sz´a mos´a g´a nak alapvet˝o eszk¨o zei,ezek k¨o z¨u l k¨u l¨o n kiemelj¨u k a besz´e dfelismer´e st´e s a HMM alap´u g´e pi ford´ıt´a st[1].A Markov modellez´e s nyelvtechnol´o giai haszn´a lhat´o s´a g´a t a franci´a t´o l a k´ınaiig m´a r sz´a mos nyelvhez k´e sz¨u lt alkalmaz´a s bizony´ıtja.A projekt m´a sodik c´e lja teh´a ta rejtett Markov modell technol´o gi´a nak alkalmaz´a sa a sz´o t´a ri t¨o bb´e rtelm˝u s´e g probl´e m´a j´a nak megold´a s´a ra.A statisztikai m´o dszer–b´a r k´e ts´e gk´ıv¨u l eredm´e nyesebb,mint a hagyom´a nyos szab´a lyrendszereken alapul´o GF–az´e rt nem csodaszer.Legfontosabb gyenges´e ge abban´a ll,hogy a rendszer meg´e p´ıt´e se kifejezetten sok adatot ig´e nyel.A statisztikai alap´u g´e pi ford´ıt´a s alapvet˝o adatforr´a sa a p´a rhuzamos korpusz.A p´a rhuzamos korpusz olyan sz¨o vegminta,amely egy adott tartalmat k´e t nyel-ven jelen´ıt meg´e s a nyelvi egys´e gek(p´e ld´a ul mondatok)sorrendileg illesztve vannak egym´a shoz.A projekt harmadik c´e lja magyar-angol p´a rhuzamos korpusz l´e trehoz´a sa.P´a rhuzamos k´e tnyelv˝u sz¨o vegkorpusz k´e sz´ıt´e s´e nek bevett m´o dja sz´e pirodalmi sz¨o vegek´e s ig´e nyes m˝u ford´ıt´a saik gy˝u jt´e se´e s illeszt´e se.Ez a statisztikai alap´u GF m´o dszerhez sz¨u ks´e ges adatmennyis´e gnek csup´a n t¨o red´e k´e t(n´e h´a ny sz´a z megabyte-ra tehet˝o anyagot)k´e pes ny´u jtani.Enn´e l nagyobb baj,hogy az el´e rhet˝o irodalmi jelleg˝u forr´a sok(pl.a Biblia vagy Orwell1984c´ım˝u reg´e nye)a gyakorlati(nyers)ford´ıt´a shoz nem megfelel˝o ek.Mivel a gyakorlati g´e pi ford´ıt´a s legfontosabb c´e lsz¨o vegei¨u zleti,technol´o giai´e s jogi tartalmak,elengedhetetlen, hogy a sz¨o vegkorpusz ezeknek a ter¨u leteknek a jellemz˝o szaksz´o kincs´e t min´e l nagyobb mennyis´e gben tartalmazza.A c´e l nem lehet Miksz´a th angolra ford´ıt´a sa, hiszen ilyesmire v´a llalkozni automatiz´a lt m´o dszerrel egyszer˝u en sarlat´a ns´a g lenne.Praktikus lehet viszont,hogy a magyarul ki´ırt tenderek angol nyelven is el´e rhet˝o ek legyenek,ami lehet˝o v´e tenn´e a besz´a ll´ıt´o k k¨o r´e nek n¨o veked´e s´e t,´e s a magyar vev˝o potenci´a lisan t¨o bb´e s jobb aj´a nlat k¨o z¨u l v´a laszthatna.A korpusz el˝o´a ll´ıt´a s´a n´a l´ıgy els˝o sorban nem a sz´e pirodalmi sz¨o vegekre,hanem a vil´a gh´a l´o n tal´a lhat´o t¨o bbnyelv˝u szerverekre koncentr´a ln´a nk(l.[3]).El˝o zetes becsl´e seink sz-erint ett˝o l egy nagys´a grenddel nagyobb,´e s persze gyakorlati szempontb´o l sokkal hasznosabb,p´a rhuzamos korpusz v´a rhat´o.References1.Brown,Peter F.,Della Pietra,Stephen,Della Pietra,Vincent J.,Mercer,RobertL.:The Mathematic of Statistical Machine Translation:Parameter Estimation.In Computational Linguistics19(1994)263–311.2.ALPAC1966:Languages and machines:computers in translation and linguistics.A report by the Automatic Language Processing Advisory Committee,Divisionof Behavioral Sciences,National Academy of Sciences,National Research Coun-cil.Washington,D.C.:National Academy of Sciences,National Research Council.(Publication1416.).3.Resnik,Philip:Mining the Web for Bilingual Text.Proceedings of the InternationalConference of the Association of Computational Linguistics.Maryland.(1999)。

自主迁移的并行遗传算法用于马斯京根模型参数估计

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ｆｌｉｔｃｌｐｉａ．ｉｙｔｅｐｒｌｌｅｈｏｏｙａｄｔｅｇｎｔｌｏｉｈ，ａｓｏｄｃｓｔｅｒｓａｃｎｖｅｏｈｆｕａｌｎｏｌａｔｏｏｍ１Ｕｎｆｈａａｌｃｎｌｇｎｈｅｅｉａｇｒｔｍｅｔｃｌｏｃｎｕｔｈｅｅｒｈｉｉｗｆｔｅｉｌ — ｎ
传算法优化模型参数的过程，应用实例进行检并
验，验结果表明本文算法是一种有效求解马斯京检根模型参数的方法。
单，计算快捷，对河道地形和糙率资料要求低，在且
一
般的河道洪水中演算效果较好，因而已在世界上
传算法相结合，针对影响并行遗传算法性能的迁移时机进行研究，出自主迁移的并行遗传算法用于马且提
斯京根模型参数估计。实验结果表明，该算法为估计马斯京根模型参数提供了一种有效的方法。
关键词：自主迁移；并行遗传算法；马斯京根模型；参数估计中圈分类号：１ＴＰ８文献标识码：Ａ
１引言பைடு நூலகம்
马斯京根流量演算法是Ｍｃａｔｙ于１３～Ｃｒｈ９４１３９５年在美国马斯京根河上首先提出和应用的一种洪水流量演算方法［，１由于该方法数学上比较简］

杭州电子科技大学2017年博士生导师介绍颜成钢

杭州电子科技大学2017年博士生导师介绍颜成钢一、导师照片颜成钢二、基本信息颜成钢Yan Chenggang教授所属学院：自动化学院导师类别：博士生导师、硕士生导师研究方向：模式识别与智能系统（智能信息处理和计算摄像学）博士招生学院：自动化学院硕士招生学院：自动化学院联系方式：cgyan@三、个人简述颜成钢，男，1984年11月出生，浙江省杭州市人，教授，博士生导师。

2013年博士毕业于中国科学院计算技术研究所；2012年到2013年期间，在微软亚洲研究院访问交流；2014年到2016年期间，在清华大学从事助理研究员（博士后）工作。

主要从事计算摄像学和智能信息处理的研究。

主持和参与多个国家项目，已发表SCI论文近20篇。

获得国际会议《International Conference on Game Theory for Networks》最佳论文奖、国际会议《SPIE/COS Photonics Asia Conference9273》最佳论文奖、国际会议《International Conference on Multimedia and Expo》最佳论文提名奖、中科院院长奖学金特别奖、中科院计算所所长奖学金特别奖。

长期与海内外知名高校研究所和企业保持良好的合作关系，培养了大批优秀的本科生和研究生。

四、学术成果（一）代表性论文1.C.G.Yan,F.Dai,et al.,“Parallel Deblocking Filter for H.264/AVC implemented on Tile64 Platform”,International Conference on Multimedia and Expo,2011.(最佳论文提名奖)2.C.G.Yan,Y.D Zhang,et al.,“A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors”,IEEE Signal Processing letters,2014.（ESI高被引论文）3.C.G Yan,Y.D Zhang,et al.,“Highly Parallel Framework for HEVC Motion Estimation on Many-core Platform”,Data Compression Conference,2013.(领域顶级国际会议)4.X.Chen,C.G Yan,et al.,“WBSMDA:Within and Between Score for MiRNA-Disease Association prediction”,Scientific Reports,2015.(Nature旗下期刊)5.C.S.Han（指导的学生）,et al.,“Explore spatial-temporal relations:transient super-resolution with PMD sensors”,SPIE/COS Photonics Asia Conference9273,2014.(最佳论文奖)（二）代表性科研项目1.《面向众核处理器的HEVC并行编码关键技术研究》，国家自然科学基金面上项目，20万，61472203，2015年1月-2015年12月，项目负责人2.中国博士后科学基金特别资助，15万，项目负责人3.中国博士后科学基金面上资助一等资助，2014M560086，8万，项目负责人4.《多维多尺度高分辨率计算摄像仪器》，国家自然科学基金仪器重大专项，8000万，2014年1月-2018年12月，61327902，研究骨干5.《移动互联网与宽带网融合总体架构和关键评估方法及移动互联网与电信宽带网融合技术方案及应用示范》，2012BAH06B01，国家科技支撑计划，935万，2012年1月-2014年12月，研究骨干（三）知识产权“Visual C++2010开发权威指南”，人民邮电出版社，版权出版到台湾五、主要荣誉1.2014年中科院院长奖学金特别奖，中科院研究生教育的最高奖励；2.2014年国际会议SPIE/COS Photonics Asia Conference9273最佳论文奖；3.2014年国际会议International Conference on Game Theory for Networks最佳论文奖；4.2013年博士研究生国家奖学金；5.2012年中科院计算所所长奖学金特别奖，中科院计算所研究生教育的最高奖项；6.2011年国际会议International Conference on Multimedia and Expo最佳论文提名奖；六、学术兼职长期担任《IEEE Transactions on Image Processing》、《IEEE Transactions on Neural Networks and Learning Systems》等在内的10余个国际权威期刊审稿人。

贝叶斯超参数优化多层感知器

贝叶斯超参数优化是一种用于自动调整机器学习模型超参数的优化技术。

它使用贝叶斯概率理论来估计超参数的最佳值，以优化模型的性能。

多层感知器（MLP）是一种常用的神经网络模型，由多个隐藏层组成，每个层包含多个神经元。

MLP可以用于分类、回归等多种任务。

当使用贝叶斯超参数优化来调整MLP的超参数时，通常会选择一些常见的超参数，如学习率、批量大小、迭代次数等。

贝叶斯优化器会根据这些超参数的性能，选择下一个可能的最佳值。

它通过在每个步骤中随机选择少量的超参数组合，而不是搜索每个可能的组合，来提高效率。

在实践中，贝叶斯超参数优化通常使用一种称为高斯过程回归（Gaussian Process Regression）的方法，该方法可以估计每个超参数的可能值以及它们的概率分布。

然后，根据这些信息选择下一个超参数的值，以最大化模型性能的预期改善。

使用贝叶斯超参数优化可以自动调整超参数，避免了手动调整的困难和耗时。

此外，它还可以帮助找到更好的超参数组合，从而提高模型的性能和准确性。

这对于机器学习任务的实验和开发非常重要，因为它可以帮助快速找到最佳的模型配置。

mindgpt 参数

mindgpt 参数
MINDgpt是一个基于OpenAI的GPT-3模型进行自然语言处理的模型。

它的参数包括以下几个方面：
1. 模型深度（Model Depth）：指模型的层数，即有多少个Transformer Encoder层。

层数越多，模型的表达能力越强，但计算成本也会增加。

2. 序列长度（Sequence Length）：指输入到模型中的文本序列的最大长度。

较大的序列长度可以捕捉更多的上下文信息，但也增加了计算成本。

3. 隐藏单元数（Hidden Units）：指每个Transformer Encoder 层中隐藏层的维度。

维度越高，模型的表达能力越强，但计算成本也会增加。

4. Embedding维度（Embedding Dimension）：指输入token的表示维度。

维度越高，模型可以捕获更多的语义信息，但计算成本也会增加。

5. 批次大小（Batch Size）：指每个训练迭代中用于更新模型参数的样本数目。

较大的批次大小可以提高训练效率，但也需要更多的内存和计算资源。

这些参数可以根据具体任务和计算资源进行调整，以达到最佳的模型性能和资源利用效率。

AI自然语言处理序列到序列模型的评估标准与指标详解

AI自然语言处理序列到序列模型的评估标准与指标详解序列到序列模型在自然语言处理领域中有着广泛的应用，如机器翻译、对话系统等。

评估这些模型的性能是衡量其有效性和可靠性的重要指标。

本文将详细介绍序列到序列模型的评估标准与指标。

1. 生成语言评估（Generation Metrics）生成语言评估是衡量模型生成语言质量的一项重要评估指标。

最常用的评估指标是BLEU（Bilingual Evaluation Understudy）指标。

BLEU 指标通过比较模型生成的文本与参考答案之间的相似程度，来评估模型的性能。

BLEU指标的取值范围在0到1之间，数值越接近1代表模型生成的语言越优秀。

2. 语法正确性评估（Syntactic Metrics）语法正确性评估是评估模型生成的句子是否符合语法规则的指标。

常用的语法正确性评估指标包括语法错误率和错误句比例。

语法错误率是将错误数量除以总句子数量得到的比例，而错误句比例是错误句子的占比。

3. 语义正确性评估（Semantic Metrics）语义正确性评估是评估模型生成的句子是否符合语义逻辑的指标。

常用的语义正确性评估指标包括语义错误率和错误句比例。

语义错误率是将错误数量除以总句子数量得到的比例，而错误句比例是错误句子的占比。

4. 流畅性评估（Fluency Metrics）流畅性评估是评估模型生成的句子是否流畅易懂的指标。

常用的流畅性评估指标包括困惑度（Perplexity）和人工评估。

困惑度是用来度量模型对给定输入的预测能力。

数值越低代表模型的预测能力越好。

人工评估则通过专家评估或众包评估来判断句子的流畅性。

5. 多样性评估（Diversity Metrics）多样性评估是评估模型生成的句子是否多样的指标。

常用的多样性评估指标包括n-gram重复率、语义重复率和主题分布评估等。

n-gram 重复率是指句子中相邻n个词的重复次数占句子总词数的比例。

语义重复率是衡量句子中语义相似度较高的词语重复次数占句子总词数的比例。

人工智能深度学习技术练习(习题卷2)

人工智能深度学习技术练习(习题卷2)第1部分：单项选择题，共50题，每题只有一个正确答案,多选或少选均不得分。

1.[单选题]关于mini-batch说法错误的是A)指的是批量梯度下降B)适用于样本量小的数据集C)每一次只运算部分数据,最后将全部数据进行运算D)适用于样本量大的数据答案:B解析:2.[单选题]RMSprop相比Momentum,可以选择更大的()A)损失函数B)学习率C)激活函数D)样本集答案:B解析:3.[单选题]如果从python一侧,想得到tf的节点S对应的值,需要下列:A)A=tf.run(S)B)A=S.valueC)A=S.eval()D)tf.assign(A,S)答案:A解析:4.[单选题]Tf定义一个占位符号的语句是A)Y = tf.zeros(2.0,shape=[1,2])B)X = tf.variable(2.0,shape=[1,2])C)Y = tf.placeholder(tf.float32)D)Y=ones(2.0,shape=[1,2])答案:C解析:5.[单选题]激活函数tf.nn.relu能A)用于卷积后数据B)用于卷积核C)用于步长D)不能用到全连接层答案:A解析:6.[单选题]Tf.convert_to_tensor用于将不同( )变成张量:比如可以让数组变成张量、也可以让列表变成张量。

A)数据B)变量C)高度答案:A解析:7.[单选题]表示数组维度的元组命令是（）。

A)ndarray.ndimB)ndarray.shapeC)ndarray..sizeD)ndarray.dtype答案:B解析:难易程度：易题型：8.[单选题]深度学习元年是（）年A)2016B)2006C)2008D)2022答案:B解析:9.[单选题]非常经典的LeNet-5神经网络其FC层处理完成以后,输出的结果会再经过那一个激活函数输出()?A)RelUB)sigmoidC)tanhD)sin答案:B解析:10.[单选题]以下哪个是门控循环单元A)LSTMB)GRUC)CNND)RNN答案:B解析:11.[单选题]当在卷积神经网络中加入池化层(pooling layer)时，变换的不变性会被保留，是吗？A)不知道B)看情况C)是D)否答案:C解析:12.[单选题]以下关于循环神经网络的说法中,哪个是正确的?A)循环神经网络可以和卷积神经网络混合使用B)循环神经网络的性能高于卷积神经网络C)循环神经网络可以代替卷积神经网络D)循环神经网络中没有BP算法答案:A解析:13.[单选题]A=2412,B=2436，求BA（）。

hinton中文版深度学习解析

深度学习Yann LeCun1,2, Yoshua Bengio3 & Geoffrey Hinton4,5深度学习是指由多个处理层组成的计算模型来学习表示具有多个抽象层次的数据。

这个方法可以显着地改进语音识别，视觉对象识别，对象检测和许多其他领域，如药物发现和基因组学的先进技术。

深度学习可以通过反向传播算法发现大数据集的复杂结构，来说明一台机器如何从前一层的特征改变其用于计算在每一层中的特征内部参数。

深度卷积网在处理图像、视频、语音和音频等方面带来了突破性的进展，而递归网络已经为顺序数据方面，如文本和语音，指明了方向。

机器学习技术促进了现代社会的许多方面：从网络搜索到社交网络的内容过滤，到对电子商务网站的建议，并且它越来越多地出现在消费类产品之中，如相机和智能手机。

机器学习系统是用来识别图像中的对象、把语音记录成文字，把新闻、海报或者产品与用户的兴趣进行匹配，并选择相关的搜索结果。

逐渐地，这些应用程序使用的一类技术就被称为深度学习。

传统的机器学习技术在处理原始形式的自然数据只有有限的能力。

几十年来，创建一个模式识别或需要精心的工程和相当大的专业知识的机器学习系统，以设计一个将原始数据（如图像的像素值）转换一个合适的内部表示或特征向量学习子系统的特征提取器，通常这可以是一个分类器，它可以在输入中检测或分类模式。

表示学习是允许一台机器被输入原始数据，并自动发现检测或分类所需特征的一组方法。

深度学习方法是一种具有多层次表示的学习方法，通过简单的非线性模块组成，每个模块转换一个级别上的特征（从原始输入开始）到一个更高，更抽象的层级上的特征。

足够多的这样的层级转换，可以学习非常复杂的功能。

对于分类任务，更高层次的表示会放大输入在区分方面的重要性和抑制不相关的变化。

例如，一个图像以像素阵列的形式出现，并且第一表示层中的学习特征通常表示图像中特定取向和位置处的边缘存在或不存在。

第二层通常通过点样颗粒排列来检测图案边缘，而不考虑边缘位置的小变化。

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Modelling of Parallel Processing Tasks by Combinatorial Designs
Abstract
1
Keywords
In computer science, we are presently facing the situation that on one hand massively parallel computers become more and more reliable so that the importance of parallel processing constantly grows, on the other hand parallel processing still su ers from the lack of theoretical concepts. Without theoretical foundations, however, parallel processing will not reach the maturity which is needed. One idea in attacking this grievance could be to look at other sciences, namely elds within mathematics, whether they can provide the necessary tools. Combinatorics is a part of discrete mathematics, which might be able to deliver most useful methods and concepts. Many articles in literature can be found, which describe the use of graph theory and combinatorics for a wide variety of applications in computer science, especially in network analysis. Nearly all of them restrict their deliberations to simple graphs; more elaborated structures, however, like hypergraphs or combinatorial designs (experimental designs) rather seldomly appear 1]. Related work mainly concentrates on the design of multiprocessor networks 2]. Here, we will show how parallel hardware and algorithms can be described and classi-
¨ FORSCHUNGSZENTRUM JULICH GmbH
Zentralinstitut fur Angewandte Mathematik ¨ D-52425 Julich, Tel. (02461) 61-6402 ¨
Interner Bericht
Modelling of Parallel Processing Tasks by Combinatorial Designs
Heribert C. Burg
KFA-ZAM-IB-9635
Dezember 1996 (Stand 23.12.96)
Dieser Bericht wurde zur Publikation eingereicht.
Heribert C. Burg Julich Research Centre, Central Institute for Applied Mathemh.burg@kfa-juelich.de
I. Introduction
2
ed by means of combinatorial designs. On the basis of this description, it is presented how important tasks within parallel processing like mapping, partitioning, embedding, and routing can be treated. As we intend to give an overview on the possibilities one has using designs within parallel processing, we cannot go too much into details. In the second section we will give a short introduction on mathematics of designs including the de nitions used in the following sections. We will then demonstrate how parallel architectures and parallel algorithms can be regarded as combinatorial designs. On the basis of this combinatorial description of parallel objects, we will show in the fourth section how tasks emerging in parallel processing can be transferred to combinatorics. It will become evident that if such transformations are possible, solutions for the tasks within parallel processing can be found by means of combinatorics. Finally, further application elds of designs within parallel processing will be indicated. Here only some necessary de nitions are given to introduce combinatorial designs, further mathematics on combinatorial designs can be taken from several books on this topic, e.g. 3]. In the context addressed here, the de nition of a combinatorial design as a pair of parameters is su cient. Sometimes it can be necessary or more elegant to de ne a design as a triple. Let V be a nite set of cardinality v. A set-system B on V is a collection of subsets of V . Elements of B are called blocks; the number of blocks is denoted by b =j B j. Let V be a nite set and B a set-system on V . Then the pair (V; B) is called a (combinatorial) design. The replication number rx of an element x 2 V is de ned by the number of blocks containing x. The design is called symmetric if v = b. If all blocks of the combinatorial design have the same number of elements, say k, the design is called k-uniform. The design is called complete if each block contains all elements of V . There are many interesting properties around designs, we will here just point out the property of balance. Choosing any subset S V , one can look whether S is a subset of any of the blocks of B. The number of blocks of B each complete containing S is called the index of S within B. It is written as (S ) 0. Looking at all subsets of V with the same number of elements, say t, the (possibly di erent) numbers of blocks containing these subsets form a set t called t-index-set of B; t := f (S ) : j S j= t; S V g. A set-sytem is called t-balanced, if its t-index-set contains exactly one element t, satisfying t > 0. A 1-balanced set-system just means, that each element of V occurs equally often within the blocks of B. Every set-system is 0-balanced, with 0 being the number of blocks. A t-balanced k-uniform set-system is (t ? 1)-balanced as well. Look at the following design example (V; B): V = f0; 1; 2; 3; 4; 5; 6g, B = f(0; 1; 2); (2; 3; 4); (4; 5; 0); (0; 6; 3); (1; 6; 4); (2; 6; 5); (1; 3; 5)g. This combinatorial design is 2-balanced, since each 2-element-subset of V appears equally often (once) within blocks of the set-system B. With these de nitions we come to the most important structure within combinatorics of designs: A balanced incomplete block-design (BIBD) is a pair (V; B), with B