Noise-contrastive estimation: A new estimation principle ...

ated 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.

  Into, Noise, Estimation, Contrastive, Noise contrastive estimation, Noise contrastive estima tion, Estima, Timation

TPOT: A Tree-based Pipeline Optimization Tool for ...

JMLR: Workshop and Conference Proceedings 64:66{74, 2016 ICML 2016 AutoML Workshop TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine …

  Automating, Machine, Tool, Pipeline, Optimization, Pipeline optimization tool for automating machine

Ensembles for Time Series Forecasting

Ensembles 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.

  Series, Time, Time series, Forecasting, Beslenme, Ensembles for time series forecasting

Show, Attend and Tell: Neural Image CaptionGeneration …

Show, 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?

  Image, Attention, Neural, Tell, And tell, Neural image captiongeneration, Captiongeneration

Wasserstein Generative Adversarial Networks

Wasserstein 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.

  Network, Adversarial, Generative, Wasserstein generative adversarial networks, Wasserstein

Self-Attention Generative Adversarial Networks

Self-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.

  Network, Self, Attention, Adversarial, Generative, Self attention generative adversarial networks

Generative Adversarial Text to Image Synthesis

deep 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 ...

  Image, Texts, Decoder, Synthesis, Deep, Encoder, Convolutional, Text to image synthesis, Deep convolutional decoder

On the di culty of training recurrent neural networks

On 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.

Deep Gaussian Processes

representational 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 ...

  Process, Gaussian, Gaussian process

Gender Shades: Intersectional Accuracy Disparities in ...

117 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).

  Enforcement, Gender, Shades, Stopped, Forcement, Stopped by law enforcement, Law en forcement, Gender shades

Understanding Contrastive Representation Learning through ...

The term contrastive loss has also been generally used to refer to various objectives based on positive and negative samples, e.g., in Siamese networks (Chopra et al., 2005; Hadsell et al., 2006). In this work, we focus on the spe-cific form in Equation (1) that is widely used in modern unsupervised contrastive representation learning literature.

  Contrastive

Dense Contrastive Learning for Self-Supervised Visual Pre ...

contrastive loss, which extends the conventional InfoNCE loss [29] to a dense paradigm. With the above approaches, we perform contrastive learning densely using a fully con-volutional network (FCN) [26], similar to target dense pre-diction tasks. Our main contributions are thus summarized as follows.

  Contrastive

DetCo: Unsupervised Contrastive Learning for Object Detection

contrastive learning [5,19,5,3,18] currently achieved state-of-the-art performance, arousing extensive attention from researchers. Unlike generative methods, contrastive learning avoids the computation-consuming generation step by pulling representations of different views of the same im-age (i.e., positive pairs) close, and pushing representations

  Contrastive

Exploring Simple Siamese Representation Learning

Contrastive learning. The core idea of contrastive learn-ing [16] is to attract the positive sample pairs and repulse the negative sample pairs. This methodology has been recently popularized for un-/self-supervised representation learning [36,30,20,37,21,2,35,17,29,8,9]. Simple and effective instantiations of contrastive learning have been ...

  Learning, Simple, Representation, Contrastive, Assieme, Simple siamese representation learning

Bootstrap Your Own Latent A New Approach to Self ...

Contrastive approaches avoid a costly generation step in pixel space by bringing representation of different views of the same image closer (‘positive pairs’), and spreading representations of views from different images (‘negative pairs’) apart [39, 40]. Contrastive methods often require

  Your, Talent, Bootstrap, Contrastive, Bootstrap your own latent

Mother-Tongue Interference in the Acquisition of English ...

Contrastive analysis is concerned with the study of a pair of languages with the aim of discovering their structural similarities and differences. Contrastive Analysis is a method that was widely used in the 1960s and early 1970s to explain why some features of a target language were more difficult to learn than others. (Mozlan, 2015)

  Contrastive

Supervised Contrastive Learning - NIPS

contrastive learning which uses only a single positive). These positives are drawn from samples of the same class as the anchor, rather than being data augmentations of the anchor, as done in self-supervised learning. While this is a simple extension to the self-supervised setup, it is non-

  Contrastive

Self-Prediction and Contrastive Learning

Contrastive Learning: Inter-Sample Classification Given both similar (“positive”) and dissimilar (“negative”) candidates, to identify which ones are similar to the anchor data point is a classification task. There are creative ways to construct a set of data point candidates: 1. The original input and its distorted version

  Contrastive

Dimensionality Reduction by Learning an Invariant Mapping

A contrastive loss function is employed to learn the param-eters W of a parameterizedfunction GW, in such a way that neighborsare pulled togetherand non-neighborsare pushed apart. Priorknowledgecan beused to identifythe neighbors for each training data point. The method uses an energy based model that uses the

  Reduction, Learning, Contrastive, Dimensionality, Dimensionality reduction by learning an