Generative Adversarial Nets - NIPS

Generative 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.

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks, Adversar ial, Adversar

On Discriminative vs. Generative Classifiers: A …

On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng Computer Science Division University of California, Berkeley

  Comparison, Logistics, Regression, Logistic regression

SAGA: A Fast Incremental Gradient Method With Support for ...

SAGA is preferred over SVRG both theoretically and in practice. For neural networks, where no theory is available for either method, the storage of gradients is generally more expensive than the

  With, Methods, Support, Fast, Saga, Derating, Incremental, A fast incremental gradient method with support

Thinking Fast and Slow with Deep Learning and Tree Search

System 1 is a fast, unconscious and automatic mode of thought, also known as intuition or heuristic process. System 2, an evolutionarily recent process unique to humans, is a slow, conscious, explicit

  With, Learning, Search, Tree, Thinking, Deep, Fast, Slow, Thinking fast and slow with deep learning and tree search

A Growing Neural Gas Network Learns Topologies

A Growing Neural Gas Network Learns Topologies 627 a) Delaunay triangulation b) induced Delaunay triangulation Figure 1: Two ways of defining closeness among a set of points.

  Network, Neural, Topologies, Neural gas network

Attention is All you Need - Neural Information Processing ...

Attention Is All You Need Ashish Vaswani Google Brain avaswani@google.com Noam Shazeer Google Brain noam@google.com Niki Parmar Google Research nikip@google.com

  Needs, Attention, Neural, Attention is all you need

ImageNet Classification with Deep Convolutional Neural ...

Challenge, 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 ...

  Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge

Time-series Generative Adversarial Networks

A 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 ...

  Network, Adversarial, Generative, Generative adversarial networks

Hidden Technical Debt in Machine Learning Systems

account 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

  System, Design, Machine, Technical, Learning, Debt, Hidden, Hidden technical debt in machine learning systems

Character-level Convolutional Networks for Text Classification

Applying 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.

  Network, Convolutional, Convolutional networks, Semantics

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

  Information, System, Processing, Inps, Neural, Neural information processing systems

2007 NIPS Tutorial on: Deep Belief Nets

<>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.

  2007, Tutorials, Deep, Inps, Nets, Belief, 2007 nips tutorial on, Deep belief nets

Sentence-BERT: Sentence Embeddings using Siamese BERT …

n(n 1)=2 = 49995000inference computations. On a modern V100 GPU, this requires about 65 hours. Similar, finding which of the over 40 mil-lion existent questions of Quora is the most similar for a new question could be modeled as a pair-wise comparison with BERT, however, answering a sin-gle query would require over 50 hours.

Random Features for Large-Scale Kernel Machines

1 Q d 1 π(1+ω2 d) Cauchy Q d 2 1+∆2 d e−k∆k 1 Figure 1: Random Fourier Features. Each component of the feature map z( x) projects onto a random direction ω drawn from the Fourier transform p(ω) of k(∆), and wraps this line onto the unit circle in R2.

  Feature

A Simple Unified Framework for Detecting Out-of ...

1.0 FPR on out-of-distribution (TinyImageNet) 0 0.5 1.0 0.85 0.90 0.95 1.00 0 0.2 0.4 (c) ROC curve Figure 1: Experimental results under the ResNet with 34 layers. (a) Visualization of final features from ResNet trained on CIFAR-10 by t-SNE, where the colors of points indicate the classes of the corresponding objects.

Kernel Descriptors for Visual Recognition

The hard binning underlying Eq. 1 is only for ease of presentation. To get a kernel view of soft binning [13], we only need to replace the delta function in Eq. 1 by the following, soft –(¢) function: –i(z) = max(cos(µ(z)¡ai)9;0) (4) where a(i) is the center of the i¡th bin. In addition, one can easily include soft spatial binning by