hierarchical cross entropy

/Type /Page endobj /Type /Page /Parent 1 0 R /Type (Conference Proceedings) It can be learned using a variety of language models. 3 0 obj /Book (Advances in Neural Information Processing Systems 31) Unfortunately, extensive numerical experiments indicate that the standard practice of training neural networks via stochastic gradient descent with random starting points often drives down the hierarchical loss nearly as much when minimizing the standard cross-entropy loss as when trying to minimize the hierarchical loss directly. optimization technique /Rotate 0 /Resources 638 0 R hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. /Annots 35 0 R /Language (en\055US) Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. /firstpage (8778) Full text Get a printable copy (PDF file) of the complete article (772K), or click on a … They advocate for a more widespread use of the AHC for evaluating models, and detail two simple baseline classiﬁcation modules able to decrease the AHC of deep models: Soft-Labels and Hierarchical Cross-Entropy. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. sensitivity-based nonlinear optimization !$%P�!�`��p%ʾ賐RF�4�|!�@��^zE�e�� capacitor budgeting cross-entropy optimization power supply noise Therefore, fused results show better integrated performances. << >> /Contents 377 0 R << /Parent 1 0 R In this paper, we propose a hierarchical cross-entropy based optimization tech- nique which is more efficient and parallel-friendly. /Author (Zhilu Zhang\054 Mert Sabuncu) /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Annots 385 0 R /Date (2018) /Resources 665 0 R Introduction¶. 13 0 obj /Rotate 0 /Type /Page Cross-entropy loss is used when adjusting model weights during training. So I am thinking about changing to One Hot Encoded labels. Cross Entropy. /Parent 1 0 R ��Ǫ� It can be learned using a variety of language models. endobj Before I was using using Cross entropy loss function with label encoding. endobj Cross-entropy loss is the negative 30 of the logarithm of our hierarchical win when the hierarchy is \flat," that is, when the 31 hierarchy is the degenerate case in which all classes are leaves attached to the same root. Cross-entropy is defined as. /Count 11 D-Softmax is based on the intuition that not all words require the same number of parameters: Many occurrences of frequent words allow us to fit many parameters to them, whil… /Annots 646 0 R I’ve also read that Cross Entropy Loss is not ideal for one hot encodings. Binary Cross-Entropy Loss. /Type /Catalog >> << 02/06/2021 ∙ by Jie Mei, et al. /Annots 591 0 R /MediaBox [ 0 0 612 792 ] /Rotate 0 Also called Sigmoid Cross-Entropy loss. abstract decoupling capacitor Figure 2 shows binary cross entropy loss functions, in which p is the predicted probability and y is the label with value 1 or 0. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. It is a Sigmoid activation plus a Cross-Entropy loss. Word embedding is a dense representation of words in the form of numeric vectors. /Annots 519 0 R /Type /Page /MediaBox [ 0 0 612 792 ] hierarchical distance of top-k predictions across datasets, with very little loss in accuracy. endobj /Rotate 0 sensitivity-guided cross-entropy /MediaBox [ 0 0 612 792 ] endobj For reasons explained later on, the loss function in is commonly called the cross-entropy loss.Since $\mathbf{y}$ is a one-hot vector of length $q$, the sum over all its coordinates $j$ vanishes for all but one term. /Resources 290 0 R endobj /Filter /FlateDecode /Contents 15 0 R Cross-entropy loss increases as the predicted probability diverges from the actual label. /Resources 79 0 R The main reason is that the architecture involves the simultaneous training of two models: the generator … To achieve high eﬃciency, a sensitivity-guided cross entropy (SCE) al-gorithmisproposedwhichintegratesCEwithapartitioning- /Annots 441 0 R Before I was using using Cross entropy loss function with label encoding. The word embedding representation is able to reveal many hidden relationships between words. /Published (2018) Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. << endobj Xueqian Zhao Note the log is calculated to base 2. The proposed method is found to outperform the baseline cross entropy based models at both levels of the hierarchy. The generative adversarial network, or GAN for short, is a deep learning architecture for training a generative model for image synthesis. /MediaBox [ 0 0 612 792 ] /Pages 1 0 R /Type /Page Figure 2 shows binary cross entropy loss functions, in which p is the predicted probability and y is the label with value 1 or 0. ��ʟ��Y$��NRY�@�;��H��@!��D4ɵ��v'Sk�!À%�+�d��:��e�{��ݭ5�zd��[(�9�z��5 �FքqA��z/AǗLy:��a��XH!,�o�:�~��0Gr�SL(�;t;��k��&ഉ}��i��F7��{� ��j�d#��Ŵa�2�=є��Cٚ�/��q��K_��kr?H��}|��ew�H>�B�n��+�88DҀt��қ�6�:V&��(��;�o ��ή_Q��J1IX�r��,��p2�N�|�htNB�W=�5M�{�Ң)IN�G�c}_7R��bN��3ꈚA�]��U�y�f��{��l��C�"�W"ǧ4�̍�sڔ7��sP�sl��Y�jj܋�ɮ�r�?&Qk��d-`H>Xd?��b,��qhQ1�]!��"��B�M+-��X�G(,� << Since CRM does not require retraining or ﬁne-tuning of any hyperpa-rameter, it can be used with any off-the-shelf cross-entropy trained model. Compared to SCE-LHS, in similar runtime, SCE-SIS can lead to 16.8 % further reduction on the total power supply noise. /ModDate (D\07220190219010640\05508\04700\047) The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor (k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate … @MISC{Zhao_hierarchicalcross-entropy, author = {Xueqian Zhao and Yonghe Guo and Xiaodao Chen and Student Member and Zhuo Feng and Shiyan Hu and Senior Member}, title = {Hierarchical Cross-Entropy Optimization for Fast On-Chip Decap Budgeting}, year = {}}. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. /Resources 506 0 R o�5`١k��'^��Lx��z�X*��,P21Z��>}*�Z��i�0�k��=��X��'��=|��ӈq+@�ɘi>�Q�]��pb�I�C߰��E1ftcVC8�X��u�,��Luf2]��-��B�� Y�p��x@�%��t�}�41�" ��U=Eg��&�� /Producer (PyPDF2) /MediaBox [ 0 0 612 792 ] /Resources 427 0 R /MediaBox [ 0 0 612 792 ] x�Zɒ��+�dW��.��JE�N�lI45� �jI��Vߒs�}Iɮ^�Û�|�}��W��N��^�^�f~�y�n��|�|s��q$m��s7'I37��G��w�kg��?U��6�=g��q��v�`��M�n�4r�}Q�u�`{��ֹ��WNc7�B'�s��+��N��e5>⾸w��6]��ӌ��K�U�i��`��|'a�&a��=�q^A2�t��I�Și��n;��A�^��}ݜ�~��a��O�m�*��RPuj��^�g�n��N�ng��8�d��ʱ�K˴Ͷhgiw��i�&�=k궪z��׏ |7B'�|7�rC�|� The GAN architecture is relatively straightforward, although one aspect that remains challenging for beginners is the topic of GAN loss functions. (2015) introduce a variation on the traditional softmax layer, the Differentiated Softmax (D-Softmax). >> /MediaBox [ 0 0 612 792 ] << These models extract salient sentences and then rewrite (Chen and Bansal,2018; Bae et al.,2019), compress (Lebanoff et al.,2019; Assume output tree path of 1 input is [A1-> A10-> A101], then loss_of_that_input = softmax_cross_entropy(A1|Ax) + softmax_cross_entropy(A10|A1x) + softmax_cross_entropy(A101|A10x) – Viet Phan Nov 28 '17 at 9:42 However, I read that label encoding might not be a good idea since the model might assign a hierarchal ordering to the labels. , /Contents 637 0 R ∙ National Oceanic and Atmospheric Administration ∙ University of Washington ∙ 0 ∙ … /Type /Pages In this post, we'll focus on models that assume that classes are mutually exclusive. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. /Created (2018) >> >> , 11 0 obj /Contents 662 0 R decap optimization solution quality >> The multi-task model is also able to learn better audio representations as observed in our clustering experiments. Equation 2: Mathematical definition of Cross-Entopy. endobj Senior Member, The College of Information Sciences and Technology. 47th DAC, June 17th, 2010 Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Two groups typify the two poles of the entropy/hierarchy dichotomy: the Glow-Wights and the Putus Templar. /Rotate 0 cg method hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. However, I read that label encoding might not be a good idea since the model might assign a hierarchal ordering to the labels. /Type /Page It can be calculated as where o ∈ {0, 1} N is the vector of observations, p ∈ [0, 1] N is the vector of predicted class probabilities, and w ∈ (0, 1) N is the vector of weights with . Therefore, higher level features show more anti-noise capabilities. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. /Resources 378 0 R /EventType (Poster) rare event probability theory 15 0 obj Finally, this average entropy was used as an aid to judgment in deciding which of the many classifications in the hierarchy yields the most reasonable groupings of hospitals. Recently, the popular solution is to build a summarization system with two-stage decoder. x�c_ ��D,C?f�s�5��% �|�䇾;�;�� Wf��.΂�|U�^BpA$σ"\��p�qo��F"�w/$F��kL�-�� $� `��Í��4��B��r��e�o��̈́�F3�׃-�7m{��"�Z�j mh�U��UӀ��ˍ��*c��i��J@9>��h��O!��@0#"݉�E��:7t�t�R�v�N�Ů��>";�#]'g"c��%e��M��H5̇*7��| S��<80V'�&�A��E��ھ:vLYf��#2�Zn��΍f�0�S3�^�I�뻃pm%��Ie��N�A��k��[�Ӱ6uCe�۳gX]�Y��>y��_ޮ3��C , You might recall that information quantifies the number of bits required to encode and transmit an event. /Editors (S\056 Bengio and H\056 Wallach and H\056 Larochelle and K\056 Grauman and N\056 Cesa\055Bianchi and R\056 Garnett) Fused hierarchical features can be treated as neutralization which aggregates the multiple level features from coarse to fine. HyPursuit is a new hierarchical network search engine that clusters hypertext documents to structure a given information space for browsing and search activities. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient (CG) methods, which can be prohibitively expensive for large-scale decap budgeting problems and cannot be easily parallelized. 6 0 obj /Rotate 0 Abstract—Decoupling capacitor (decap) has been widely used to effectively reduce dynamic power supply noise. -��ng�Į��i>��N�k�K#�L\6 �$�*��wc�� B��b�g��~�'NQ�,�EY�~_��r"��S�0�O?�X�� c\��=��Am9q_ ~{��&�61��8��h��U.��ypᅡS��5��}=�ܱ��ڊ�CE=�&�]�]��`�0��7�O��}1m ��Z��澪�-qܱ�$�t�jM��'�=箋�8W&x)�^-B|uzL�T=�#)Ķֽv�B'�(��$� index term adjoint sensitivity analysis Video-based Hierarchical Species Classification for Longline Fishing Monitoring. Student Member While softmax is [math]O(n)[/math] time, hierarchical softmax is [math]O(\log n)[/math] time. << /Contents 664 0 R cross-entropy loss with the rewards from policy gradient to directly optimize the evaluation metric for the summarization task. similar runtime 32 Extensive experiments demonstrate the advantages of hierarchical loss in comparison to 33 the conventional cross-entropy. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. Recently, the popular solution is to build a summarization system with two-stage decoder. /Contents 505 0 R Lower probability events have more information, higher probability events have less information. Cross-entropy (CE) … /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R ] To achieve the high efficiency, a sensitivity-guided cross-entropy (SCE) algorithm is introduced which integrates CE with a partitioning-based sampling strategy to effectively reduce the solution space in solving the large-scale decap … large-scale decap budgeting problem I’ve also read that Cross Entropy Loss is not ideal for one hot encodings. endobj CE is an advanced op-timization framework which explores the power of rare-event probability theory and importance sampling. 34 4 0 obj hierarchical cross-entropy advanced optimization framework /Type /Page Word embedding is a dense representation of words in the form of numeric vectors. /Contents 289 0 R hierarchical cross entropy (CE) optimization technique for solvingthedecapbudgetingproblem. /Type /Page /Resources 16 0 R >> Tl;dr: Hierarchical softmax is a replacement for softmax which is must faster to evaluate. /Rotate 0 << The aim is to minimize the loss, i.e, the smaller the loss the better the model. Unlike Softmax loss it is independent for each vector component (class), meaning that the loss computed for every CNN output vector component is not affected by other component values. << >> << endobj >> /lastpage (8788) ��M��|9g��@�S�zИ��v}[5��m�Z��e��"o=��z��7��S+�P�ڝ�� Vp�{�q9h��p��؂�UC��j�9��ǆDc��'��%��?jP�s�@�0��G��%��ڡ6yD��m'ӊ� ��M�s�l�}�rxsC�{_��\��E��+0��E�f�C�,{�SYV�p��m`�U�u�m�p��B~ 9�,[Z�D?X��Q� �o��v�m��7�-�ס�mH�x��ꡬ��Zl��B/��F�=�Al�}U�aq�p:ª�C�_�{��棫�)(G�.�� K�7 �z+�}6A\�{�!��U;�7z��8��D��(z��S�� ;a�á4�SCz��D�h /Type /Page traditional decap, Developed at and hosted by The College of Information Sciences and Technology, © 2007-2019 The Pennsylvania State University, by Cross-entropy loss increases as the predicted probability diverges from the actual label. /Contents 426 0 R ��HKE��|�1��b�j+��*zM��,�U�ڛ��׬$|�P^\��:�. This is very similar to language modelling, where the task is to predict the nextword by the words that precede it. Parallel Hierarchical Cross Entropy Optimization for On-Chipppgg Decap Budgeting Xueqian Zhao Yonghe GuoYonghe Guo Zhuo Feng Shiyan Hu Department of Electrical & Computer Engineering Michigan Technological University 1 X. Zhao 2010 ACM/EDAC/IEEE Design Automation Conference et al. We are interested then in the conditional distribution , where ranges over some fixed vocabulary . /MediaBox [ 0 0 612 792 ] , These models extract salient sentences and then rewrite (Chen and Bansal,2018; Bae et al.,2019), compress (Lebanoff et al.,2019; 12 0 obj The CBOW learning task is to predict a word by the words on either side of it (its “context” ). Cross-entropy loss is the negative 30 of the logarithm of our hierarchical win when the hierarchy is \flat," that is, when the 31 hierarchy is the degenerate case in which all classes are leaves attached to the same root. The word embedding representation is able to reveal many hidden relationships between words. with hierarchical nomenclatures in the deep learning literature. sequential importance sampling For example, vector(“cat”) - vector(“kitten”) is similar to vector(“dog”) - vector(“puppy”). We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. high efficiency 5 0 obj Ask Question Asked 2 years, ... model which deals with different levels of classification, yielding a binary vector. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. cross-entropy loss with the rewards from policy gradient to directly optimize the evaluation metric for the summarization task. /Rotate 0 ... Hierarchical … I. fast on-chip decap budgeting partitioning-based sampling strategy ,c} be the label space. 34 << /Resources 541 0 R The ethos of the Glow-Wights is chaotic self-indulgence, antipathy to kinship, and mutagenic excess. So I am thinking about changing to One Hot Encoded labels. << Design: This is a cross-sectional study conducted in 4 hospitals in China. Compared to improved CG method and conventional CE method, SCE with Latin hypercube sampling method (SCE-LHS) can provide 2 × speedups, while achieving up to 25% improvement on power supply noise. /Annots 143 0 R Yonghe Guo Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. The Cross-entropy is a distance calculation function which takes the calculated probabilities from softmax function and the created one-hot-encoding matrix to calculate the distance. Our content-link clustering algorithm is based on the semantic information embedded in hyperlink structures and document contents. importance sampling dynamic power supply noise For example, vector(“cat”) - vector(“kitten”) is similar to vector(“dog”) - vector(“puppy”). When we develop a model for probabilistic classification, we aim to map the model's inputs to probabilistic predictions, and we often train our model by incrementally adjusting the model's parameters so that our predictions get closer and closer to ground-truth probabilities.. solution space endobj /Rotate 0 /Title (Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels) They billow like an excited gas, smashing into each other as often as anyone else, and peeling bits of order from the entities they pillage. Moreover, the model is shown to transfer well when an out-of-domain dataset is used for evaluation. ... Hierarchical … hierarchical cross-entropy optimization First, let’s talk about the softmax function. total power supply noise /Type /Page Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. >> /MediaBox [ 0 0 612 792 ] Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. /Parent 1 0 R Design: This is a cross-sectional study conducted in 4 hospitals in China. Chen et al. ross-entropy is a measure of the difference between two distribution functions. /Description-Abstract (Deep neural networks \050DNNs\051 have achieved tremendous success in a variety of applications across many disciplines\056 Yet\054 their superior performance comes with the expensive cost of requiring correctly annotated large\055scale datasets\056 Moreover\054 due to DNNs\047 rich capacity\054 errors in training labels can hamper performance\056 To combat this problem\054 mean absolute error \050MAE\051 has recently been proposed as a noise\055robust alternative to the commonly\055used categorical cross entropy \050CCE\051 loss\056 However\054 as we show in this paper\054 MAE can perform poorly with DNNs and large\055scale datasets\056 Here\054 we present a theoretically grounded set of noise\055robust loss functions that can be seen as a generalization of MAE and CCE\056 Proposed loss functions can be readily applied with any existing DNN architecture and algorithm\054 while yielding good performance in a wide range of noisy label scenarios\056 We report results from experiments conducted with CIFAR\05510\054 CIFAR\055100 and FASHION\055MNIST datasets and synthetically generated noisy labels\056) /Length 4271 , Xiaodao Chen /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) Loss functions for Hierarchical Multi-label classification? /Annots 356 0 R >> /Rotate 0 >> , CEisanadvancedop-timizationframeworkwhichexploresthepowerofrare-event probability theory and importance sampling. /Contents 540 0 R A perfect model has a cross-entropy loss of 0. >> 32 Extensive experiments demonstrate the advantages of hierarchical loss in comparison to 33 the conventional cross-entropy. 9 0 obj /Parent 1 0 R /Annots 469 0 R endobj power grid design Cross-entropy (CE) … 7�!��{��FB)Y%��o��[ � ��j�߂�e��<9Q�+��,m��)��H��l��1��m��p�M2��Dؒ*(��ݟ$��c�fK��*M��eSC�Y��9��Id��I�a��5�8uS�:��O��D & ��ش��E)2_`��fZ|�h&�+ח��P�Y{v�.��G�X�D��ћ�[��%R��z�,p� N`g�� '`vK�_IX%C��hڜ��P*6{>��d)��z_@��GqaI��I�;! /Parent 1 0 R 2 0 obj /Resources 663 0 R Shiyan Hu 8 0 obj To further improve decap optimization solution quality, SCE with sequential importance sampling (SCE-SIS) method is also studied and implemented. Index Terms—Adjoint sensitivity analysis, cross-entropy optimization, decoupling capacitor budgeting, power grid design, power supply noise. To achieve the high efficiency, a sensitivity-guided cross-entropy (SCE) algorithm is introduced which integrates CE with a partitioning-based sampling strategy to effectively reduce the solution space in solving the large-scale decap budgeting problems. /MediaBox [ 0 0 612 792 ] endobj See Section 4.3 for more details on these schemes. stream For the right target class, the distance value will be less, and the distance values will be larger for the wrong target class. /Resources 468 0 R /Contents 467 0 R conventional ce method /Parent 1 0 R In order to deal with the divergence of uncertain variables via uncertainty distributions, this paper aims at introducing the concept of cross-entropy for uncertain variables based on uncertain theory, as well as investigating some mathematical properties of this concept. 7 0 obj /Type /Page In this paper, we propose a hierarchical cross-entropy based optimization tech- nique which is more efficient and parallel-friendly. /Parent 1 0 R /Parent 1 0 R ��|��M��Xg�]b��N��N�/�)�A�"��H�מ�&i�]�kVYF$7 �Xz-|�܋�� *�Ĥ��}��)�3&j��fRV)^Q8�Ra/\��j�!�v�oŨ�10�H��:��TՈ��C�P��HK�1m;��Zaz�P��M�o��~\�l��g�'%y��X�u�(i/w��8e^}��t�L��xH�B�9��g��A��e�s��H��wCw�K��1tl��ԿA&h��O��E��J��;�> ��c��C�XD]��"$��S00�