First, let’s talk about the softmax function. /Resources 638 0 R /ModDate (D\07220190219010640\05508\04700\047) << !$%P�!�`��p%ʾ賐RF�4�|!�@���^zE�e�� power supply noise    32 Extensive experiments demonstrate the advantages of hierarchical loss in comparison to 33 the conventional cross-entropy. Yonghe Guo The word embedding representation is able to reveal many hidden relationships between words. These models extract salient sentences and then rewrite (Chen and Bansal,2018; Bae et al.,2019), compress (Lebanoff et al.,2019; /Resources 378 0 R << endobj /Resources 506 0 R /Contents 15 0 R Abstract—Decoupling capacitor (decap) has been widely used to effectively reduce dynamic power supply noise. 15 0 obj While softmax is [math]O(n)[/math] time, hierarchical softmax is [math]O(\log n)[/math] time. endobj >> To achieve high efficiency, a sensitivity-guided cross entropy (SCE) al-gorithmisproposedwhichintegratesCEwithapartitioning- endobj Therefore, fused results show better integrated performances. /Contents 426 0 R 34 /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) /Annots 143 0 R 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 (CE) … /Count 11 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. The CBOW learning task is to predict a word by the words on either side of it (its “context” ). /MediaBox [ 0 0 612 792 ] Senior Member, The College of Information Sciences and Technology. /Rotate 0 /Contents 78 0 R << /Parent 1 0 R Note the log is calculated to base 2. optimization technique    sensitivity-guided cross-entropy    Introduction¶. hierarchical distance of top-k predictions across datasets, with very little loss in accuracy. , /Resources 663 0 R 6 0 obj HyPursuit is a new hierarchical network search engine that clusters hypertext documents to structure a given information space for browsing and search activities. /lastpage (8788) 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�|� ,c} be the label space. 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. 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���� 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… /Type /Page power grid design    9 0 obj /MediaBox [ 0 0 612 792 ] hierarchical cross entropy (CE) optimization technique for solvingthedecapbudgetingproblem. /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Contents 664 0 R , , It is a Sigmoid activation plus a Cross-Entropy loss. The main reason is that the architecture involves the simultaneous training of two models: the generator … For example, vector(“cat”) - vector(“kitten”) is similar to vector(“dog”) - vector(“puppy”). /Created (2018) Cross Entropy. Recently, the popular solution is to build a summarization system with two-stage decoder. >> 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��&�� /Contents 377 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. /Rotate 0 Chen et al. So I am thinking about changing to One Hot Encoded labels. Also called Sigmoid Cross-Entropy loss. endobj >> 34 /Author (Zhilu Zhang\054 Mert Sabuncu) hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. /Producer (PyPDF2) /Resources 541 0 R To further improve decap optimization solution quality, SCE with sequential importance sampling (SCE-SIS) method is also studied and implemented. Cross-entropy (CE) … << Design: This is a cross-sectional study conducted in 4 hospitals in China. Before I was using using Cross entropy loss function with label encoding. /Resources 79 0 R Equation 2: Mathematical definition of Cross-Entopy. hierarchical cross-entropy    similar runtime    We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. x�c_ ��D,C?f�s�5��% �|�䇾;�;��� Wf����.΂�|U�^BpA$σ"\����p�qo��F"�w/$F���kL�-��� $� `��Í���4���B���r��e�o���̈́�F3�׃-�7m{���"�Z�j mh�U��UӀ��ˍ���*c��i��J@9>��h����O!���@0#"݉�E��:7t�t�R�v�N�Ů��>";�#]'g"c��%e��M��H5̇*7����| S��<80V'�&�A��E����ھ:vLYf���#2�Zn���΍f�0�S3�^�I�뻃pm%������Ie��N�A��������k���[�Ӱ6uCe�۳gX]�Y��>y����_ޮ3��C /MediaBox [ 0 0 612 792 ] @MISC{Zhao_hierarchicalcross-entropy,    author = {Xueqian Zhao and Yonghe Guo and Xiaodao Chen and Student Member and Zhuo Feng and Shiyan Hu and Senior Member},    title = {Hierarchical Cross-Entropy Optimization for Fast On-Chip Decap Budgeting},    year = {}}. decap optimization solution quality    << << /Type /Page Zhuo Feng ���ʟ���Y$���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(,� /Resources 290 0 R The GAN architecture is relatively straightforward, although one aspect that remains challenging for beginners is the topic of GAN loss functions. /MediaBox [ 0 0 612 792 ] endobj /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) /Type /Page endobj /EventType (Poster) In an ideal scenario, we are given a clean dataset D = {(x i,y i)}n i=1, where each (x i,y i) 2 (X⇥Y). /firstpage (8778) /Type /Page ross-entropy is a measure of the difference between two distribution functions. Ask Question Asked 2 years, ... model which deals with different levels of classification, yielding a binary vector. >> /Parent 1 0 R Shiyan Hu /Type /Page Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. See Section 4.3 for more details on these schemes. endobj You might recall that information quantifies the number of bits required to encode and transmit an event. >> Tl;dr: Hierarchical softmax is a replacement for softmax which is must faster to evaluate. ... Hierarchical … /Length 4271 ��Ǫ� Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. Cross-entropy is a measure of the difference between two probability distributions for a given random variable or set of events. /Annots 591 0 R /Annots 356 0 R CE is an advanced op-timization framework which explores the power of rare-event probability theory and importance sampling. /MediaBox [ 0 0 612 792 ] /Rotate 0 /Rotate 0 /Rotate 0 importance sampling    14 0 obj /Annots 385 0 R hierarchical cross-entropy optimization    I’ve also read that Cross Entropy Loss is not ideal for one hot encodings. >> Lower probability events have more information, higher probability events have less information. /Rotate 0 /Book (Advances in Neural Information Processing Systems 31) Compared to SCE-LHS, in similar runtime, SCE-SIS can lead to 16.8 % further reduction on the total power supply noise. Two groups typify the two poles of the entropy/hierarchy dichotomy: the Glow-Wights and the Putus Templar. Word embedding is a dense representation of words in the form of numeric vectors. So I am thinking about changing to One Hot Encoded labels. 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. Cross-entropy loss is used when adjusting model weights during training. /Contents 505 0 R endobj %PDF-1.3 index term adjoint sensitivity analysis    cg method    Word embedding is a dense representation of words in the form of numeric vectors. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. , 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 . /Parent 1 0 R rare event probability theory    << Throughout the refinement, we use the the cross-entropy as objective function for a binary outcome, also known as log-loss, weighted by the class proportions. Therefore, higher level features show more anti-noise capabilities. sensitivity-based nonlinear optimization    << 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. /Parent 1 0 R stream 3 0 obj << abstract decoupling capacitor    /Parent 1 0 R >> 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. endobj In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. /MediaBox [ 0 0 612 792 ] << However, I read that label encoding might not be a good idea since the model might assign a hierarchal ordering to the labels. endobj Cross-Entropy ¶ Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. , Design: This is a cross-sectional study conducted in 4 hospitals in China. Loss functions for Hierarchical Multi-label classification? total power supply noise    >> Moreover, the model is shown to transfer well when an out-of-domain dataset is used for evaluation. 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. 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. /Title (Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels) '`vK�_IX%C��hڜ���P*6{>��d)��z_@��GqaI���I�;! Cross-entropy loss increases as the predicted probability diverges from the actual label. 10 0 obj >> The word embedding representation is able to reveal many hidden relationships between words. >> 02/06/2021 ∙ by Jie Mei, et al. /Parent 1 0 R I. fast on-chip decap budgeting    /Type /Page CEisanadvancedop-timizationframeworkwhichexploresthepowerofrare-event probability theory and importance sampling. The ethos of the Glow-Wights is chaotic self-indulgence, antipathy to kinship, and mutagenic excess. 47th DAC, June 17th, 2010 We are interested then in the conditional distribution , where ranges over some fixed vocabulary . Hierarchical clustering analysis (HCA) and complex system entropy clustering analysis (CSECA) were performed, respectively, to achieve syndrome pattern validation. 8 0 obj /Annots 469 0 R /Type /Page advanced optimization framework    /Rotate 0 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. endobj The generative adversarial network, or GAN for short, is a deep learning architecture for training a generative model for image synthesis. hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. -��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'�(��$� 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. Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. endobj /Parent 1 0 R /MediaBox [ 0 0 612 792 ] I need to build hierarchical tree first. 11 0 obj high efficiency    Fused hierarchical features can be treated as neutralization which aggregates the multiple level features from coarse to fine. /Annots 646 0 R /Contents 662 0 R Student Member /Pages 1 0 R Xueqian Zhao The aim is to minimize the loss, i.e, the smaller the loss the better the model. cross-entropy loss with the rewards from policy gradient to directly optimize the evaluation metric for the summarization task. /MediaBox [ 0 0 612 792 ] A perfect model has a cross-entropy loss of 0. >> capacitor budgeting    These models extract salient sentences and then rewrite (Chen and Bansal,2018; Bae et al.,2019), compress (Lebanoff et al.,2019; 7 0 obj /MediaBox [ 0 0 612 792 ] /Parent 1 0 R The proposed method is found to outperform the baseline cross entropy based models at both levels of the hierarchy. /Resources 427 0 R 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. ��p������a����P��E�5|l�c��1�j�Ia�ʉ� latin hypercube    4 0 obj >> >> /Type /Page I’ve also read that Cross Entropy Loss is not ideal for one hot encodings. 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. /Type /Page ���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 ��|��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� Our content-link clustering algorithm is based on the semantic information embedded in hyperlink structures and document contents. cross-entropy optimization    (2015) introduce a variation on the traditional softmax layer, the Differentiated Softmax (D-Softmax). /Parent 1 0 R << 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.. ��D���w�$� However, I read that label encoding might not be a good idea since the model might assign a hierarchal ordering to the labels. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. /Resources 665 0 R CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. endobj /Filter /FlateDecode 13 0 obj /Annots 35 0 R large-scale decap budgeting problem    /Contents 540 0 R << We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. << /Type /Catalog Cross-entropy loss is the negative 30 of the logarithm of our hierarchical win when the hierarchy is \flat," that is, when the 31 hierarchy is the degenerate case in which all classes are leaves attached to the same root. Cross-entropy loss increases as the predicted probability diverges from the actual label. /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R ] /Resources 468 0 R CE is an advanced op-timization framework which explores the power of rare-event probability theory and importance sampling. ��HKE��|�1�����b�j+�����*zM��,�U�ڛ��׬$|�P^\��:�. /MediaBox [ 0 0 612 792 ] << 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 441 0 R /Type (Conference Proceedings) 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. , /Published (2018) dynamic power supply noise    /Date (2018) Cross-Entropy ¶ Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. In this post, we'll focus on models that assume that classes are mutually exclusive. It can be learned using a variety of language models. ... Hierarchical … >> 2 0 obj /Type /Page 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.