Supervised Contrastive Learning - NIPS
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Figure 2: Supervised vs. self-supervised contrastive losses: The self-supervised contrastive loss (left, Eq.1) contrasts a single positive for each anchor (i.e., an augmented version of the same image) against a set of
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On Discriminative vs. Generative Classifiers: A …
papers.nips.ccOn Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science Division University of California, Berkeley
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Thinking Fast and Slow with Deep Learning and Tree Search
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A Growing Neural Gas Network Learns Topologies
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Attention is All you Need - Neural Information Processing ...
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ImageNet Classification with Deep Convolutional Neural ...
papers.nips.ccChallenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and 150,000 testing images. ILSVRC-2010 is the only version ...
Generative Adversarial Nets - NIPS
papers.nips.ccGenerative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.
Time-series Generative Adversarial Networks
papers.nips.ccA good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...
Hidden Technical Debt in Machine Learning Systems
papers.nips.ccaccount for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns. 1 Introduction As the machine learning (ML) community continues to accumulate years of experience with live
Character-level Convolutional Networks for Text Classification
papers.nips.ccApplying convolutional networks to text classification or natural language processing at large was explored in literature. It has been shown that ConvNets can be directly applied to distributed [6] [16] or discrete [13] embedding of words, without any knowledge on the syntactic or semantic structures of a language.
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Random Features for Large-Scale Kernel Machines
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Kernel Descriptors for Visual Recognition
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2007 NIPS Tutorial on: Deep Belief Nets
www.cs.toronto.edu<>1 vihj i j i j t = 0 t = 1 "=(<>0!<>1) wij#vihj vihj Start with a training vector on the visible units. Update all the hidden units in parallel Update the all the visible units in parallel to get a “reconstruction”. Update the hidden units again. This is not following the gradient of the log likelihood. But it works well.