Training Using the Cross Entropy Loss
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
... ated using 10K test pairs from the OpenSubtitles ...the training dataset in terms of both vo- cabulary and linguistic distributions (the former was cre- ated from daily conversations, whereas the latter was ...
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PORTFOLIO OPTIMIZATION USING MEASURES OF CROSS ENTROPY
... or cross entropy) measures is available, each with its own merits and ...of cross entropy and consequently provided the applications of these measures for the study of optimization principles ...
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Learning Tetris Using the Noisy Cross-Entropy Method
... The cross-entropy method is an efficient and general optimization algo- ...the cross-entropy method, using Tetris, a computer game, for ...
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Uniform Cross-entropy Clustering
... In natural way it is impossible to verify all possibles subsets which lies in the bor- ders of clusters. But we can take advantage by using Theorem 2 Therefore instead of considering one point we will use all ...
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Multilingual Seq2seq Training with Similarity Loss for Cross Lingual Document Classification
... final loss term, adding weight regularization on the encoder: L = (L src→tgt N M T + L tgt→src N M T ) + αL sim , where α needs to be chosen to balance the contri- butions from each ...similarity loss by ...
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Importance Sampling Simulations of Markovian Reliability Systems using Cross Entropy
... the cross entropy method we need to optimize the full matrix of transition probabilities, sometimes this is called a state dependent ...whether cross entropy might be applicable to find the ...
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Updating and estimating a Social Accounting Matrix using cross entropy methods
... method relative to the SAM flows. Similarly, the CE results should be closer to the original coefficient matrix. If we are seeking to use the updated SAM to estimate column coefficients, which is commonly the case when ...
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Privacy Amplification with Asymptotically Optimal Entropy Loss
... While using an error-detecting code and shortening the extractor outputs intuitively may seem to work in a straightforward manner, the proofs turn out to be quite ...Bob. Using combinatorial arguments, we ...
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Cross-Entropy Based Image Thresholding
... In this paper a thresholding method which uses Cross-Entropy Clustering instead of GMM is presented. The idea of using the CEC algorithm for thresholding problems was mentioned in [3]. This approach ...
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Cross entropy as a measure of musical contrast
... of using the information the- oretic concept of cross entropy to measure musical contrast in a symbolic context, with a focus on ...measure cross entropy using the Information ...
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Scheduling trains with cross entropy optimisation
... We assume that the trains are identical, and that any train can service any silo. Using this information, we simulate the movement of trains and calculate the time required to complete all trips. Each time a train ...
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Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis
... This would suggest that, for the analysis of the relation- ship between subjects’ walking and breathing rhythms, it appears necessary to first have an understanding of the ratio of the signal frequencies that are ...
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Measuring Coupling of Rhythmical Time Series Using Cross Sample Entropy and Cross Recurrence Quantification Analysis
... The patients with COPD mainly utilized the 1 : 1 fre- quency ratio and based on the simulated and biological results, it appears that the cRQA measures of max line, mean line, and entropy are better suited to ...
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Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method
... ing mechanism, which removes high-probability rules and places them in their own distribution with a uniform probability, allowing the algorithm to investigate the high-probability sample separate from the other rules. ...
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Assessing the spatial distribution of crop production using a generalized cross-entropy approach:
... made using expert judgments or simple area-weighting ...new, entropy-based approach to making spatially disaggregated assessments of the distribution of crop ...production. Using this approach ...
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Marginal Likelihood Estimation with the Cross Entropy Method
... More specifically, not only do existing approaches often require nontrivial programming efforts, most involve using MCMC draws to compute certain Monte Carlo averages, which are then used to derive an estimate of ...
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Implementing and simulating the cross entropy ant system
... Next, some basic checks on the route the ant has taken are performed. The time-to-live should still be above zero and the ant should not have visited the node before. In both cases, the packet is dropped. Then, a next ...
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Cross Entropy Estimation of Linear Cointegrated Equations
... to cross-entropy for the estimation of a cointegrated ...matrices, entropy can be used to estimate the parameters of this ...knowledge, entropy has the potential to yield improved estimates in ...
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Deconstructing cross-entropy for probabilistic binary classifiers
... discrimination loss. However, the calibration loss is much higher before than after logistic regression ...calibration loss, explained by the dataset shift between the NIST SRE 2010 testing data, and ...
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Portfolio selection based on fuzzy cross-entropy
... by using the fuzzy set theory, many scholars take the security returns as fuzzy variables [ 1 , 2 , 10 , 13 , 14 , 23 , 34 , 39 , 40 , 43 ...fuzzy entropy was proposed in [ 20 ] for measuring the ...
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