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Understanding Contrastive Representation Learning through ...
representation 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 …
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Noise-contrastive estimation: A new estimation principle ...
proceedings.mlr.pressated noise y. The estimation principle thus relies on noise with which the data is contrasted, so that we will refer to the new method as “noise-contrastive estima-tion”. In Section 2, we formally define noise-contrastive es-timation, establish fundamental statistical properties, and make the connection to supervised learning ex-plicit.
Deep Gaussian Processes
proceedings.mlr.pressrepresentational power of a Gaussian process in the same role is significantly greater than that of an RBM. For the GP the corresponding likelihood is over a continuous vari-able, but it is a nonlinear function of the inputs, p(yjx) = N yjf(x);˙2; where N j ;˙2 is a Gaussian density with mean and variance ˙2. In this case the likelihood is ...
TPOT: A Tree-based Pipeline Optimization Tool for ...
proceedings.mlr.pressJMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine …
Ensembles for Time Series Forecasting
proceedings.mlr.pressEnsembles for Time Series Forecasting set of real world time series. Our results clearly indicate that this is a promising research direction. In Section2we provide a brief description of the tasks being tackled in this paper.
Show, Attend and Tell: Neural Image CaptionGeneration …
proceedings.mlr.pressShow, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu? KELVIN.XU@UMONTREAL.CA Jimmy Lei Bay JIMMY@PSI.UTORONTO.CA Ryan Kirosy RKIROS@CS.TORONTO.EDU Kyunghyun Cho?
Wasserstein Generative Adversarial Networks
proceedings.mlr.pressWasserstein Generative Adversarial Networks Figure 1: These plots show ˆ(P ;P 0) as a function of when ˆis the EM distance (left plot) or the JS divergence (right plot).The EM plot is continuous and provides a usable gradient everywhere.
Self-Attention Generative Adversarial Networks
proceedings.mlr.pressSelf-Attention Generative Adversarial Networks Figure 1. The proposed SAGAN generates images by leveraging complementary features in distant portions of the image rather than local regions of fixed shape to generate consistent objects/scenarios. In each row, the first image shows five representative query locations with color coded dots.
Generative Adversarial Text to Image Synthesis
proceedings.mlr.pressdeep convolutional decoder networks to generate realistic images.Dosovitskiy et al.(2015) trained a deconvolutional network (several layers of convolution and upsampling) to generate 3D chair renderings conditioned on a set of graph-ics codes indicating shape, position and lighting.Yang et al. (2015) added an encoder network as well as actions ...
On the di culty of training recurrent neural networks
proceedings.mlr.pressOn the di culty of training recurrent neural networks @Et+1 @xt+1 Et Et+1 Et 1 xt 1 xt +1 ut +11 u tu @Et @xt @Et1 @xt1 @ xt +2 @xt +1 @x +1 x @xt1 @xt1 @xt2 Figure 2. Unrolling recurrent neural networks in time by creating a copy of the model for each time step.
Gender Shades: Intersectional Accuracy Disparities in ...
proceedings.mlr.press117 million Americans are included in law en-forcement face recognition networks. A year-long research investigation across 100 police de-partments revealed that African-American indi-viduals are more likely to be stopped by law enforcement and be subjected to face recogni-tion searches than individuals of other ethnici-ties (Garvie et al.,2016).
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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
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 …
InfoGAN: Interpretable Representation Learning by ...
papers.nips.ccrepresentation 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 ...
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
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 …