InfoGAN: Interpretable Representation Learning by ...
representation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …
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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
SAGA: A Fast Incremental Gradient Method With Support for ...
papers.nips.ccSAGA 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
Thinking Fast and Slow with Deep Learning and Tree Search
papers.nips.ccSystem 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
A Growing Neural Gas Network Learns Topologies
papers.nips.ccA 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.
Attention is All you Need - Neural Information Processing ...
papers.nips.ccAttention 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
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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Understanding Contrastive Representation Learning through ...
proceedings.mlr.pressrepresentation learning in fact directly optimizes for these two properties in the limit of infinite negative samples. We propose theoretically-motivated metrics for alignment and uniformity, and observe strong agreement between them and downstream …
InfoGAN: Interpretable Representation Learning by ...
arxiv.orgrepresentation learning [1,2], whose goal is to use unlabelled data to learn a representation that exposes important semantic features as easily decodable factors. A method that can learn such representations is likely to exist [2], and to be useful for …
A Simple Framework for Contrastive Learning of Visual ...
arxiv.orgRepresentation learning with contrastive cross entropy loss benefits from normalized embeddings and an appro-priately adjusted temperature parameter. Contrastive learning benefits from larger batch sizes and longer training compared to its supervised counterpart. Like supervised learning, contrastive learning benefits from deeper and wider ...
The Role of Visual Learning in Improving Students’ High ...
files.eric.ed.govThe visual representation of algorithms is useful both for teachers and pupils in their teaching and learning. Problem-based learning (PBL) leads to the development of higher-order thinking (HOT) skills and
DeepSDF: Learning Continuous Signed Distance Functions for ...
openaccess.thecvf.comDeepSDF: Learning Continuous Signed Distance Functions for Shape Representation Jeong Joon Park1 , 3Peter Florence 2 Julian Straub Richard Newcombe Steven Lovegrove3 1University of Washington 2Massachusetts Institute of Technology 3Facebook Reality Labs Figure 1: DeepSDF represents signed distance functions (SDFs) of shapes via latent code-conditioned …
Momentum Contrast for Unsupervised Visual Representation ...
openaccess.thecvf.comvised visual representation learning. From a perspective on contrastive learning [29] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dic-tionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the
metapath2vec: Scalable Representation Learning for ...
www3.nd.edurepresentation learning methods enable the automatic discovery of useful and meaningful (latent) features from the “raw networks.” However, these work has thus far focused on representation learning for homogeneous networks—representative of singular type of nodes and relationships. Yet a large number of social and