Understanding and Simplifying One-Shot Architecture Search

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

Show, Attend and Tell: Neural Image CaptionGeneration …

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu? [email protected] Jimmy Lei Bay [email protected] Ryan Kirosy [email protected] Kyunghyun Cho?

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

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

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

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

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

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.

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

Xception: Deep Learning With Depthwise Separable …

ules and depthwise separable convolutions are also possible: in effect, there is a discrete spectrum between regular convo-lutions and depthwise separable convolutions, parametrized by the number of independent channel-space segments used for performing spatial convolutions. A regular convolution (preceded by a 1x1 convolution), at one extreme ...

  With, Learning, Convolutions, Separable, Lution, Depthwise, Convos, Depthwise separable convolutions, Learning with depthwise separable, Convo lutions

Neural Architecture Search: A Survey

Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect ... operations like depthwise separable convolutions (Chollet, 2016) or dilated convolutions (Yu

  Learning, Convolutions, Separable, Depthwise, Depthwise separable convolutions

fchollet@google - arXiv

Depthwise separable convolutions, which our proposed architecture is entirely based upon. While the use of spa-tially separable convolutions in neural networks has a long history, going back to at least 2012 [12] (but likely even earlier), the depthwise version is more recent. Lau-rent Sifre developed depthwise separable convolutions

  Convolutions, Separable, Depthwise, Depthwise separable convolutions, Separable convolutions

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Depthwise separable convolutions are a drop-in re-placement for standard convolutional layers. Empiri-cally they work almost as well as regular convolutions but only cost: hi ·wi ·di(k 2 +d j) (1) which is the sum of the depthwise and 1 × 1 pointwise convolutions. Effectively depthwise separable convolu-

  Convolutions, Separable, Depthwise, Depthwise separable convolutions, Depthwise separable

fzhangxiangyu,zxy,linmengxiao,[email protected] arXiv ...

depthwise separable convolutions or group convolutions into the building blocks to strike an excellent trade-off between representation capability and computational cost. However, we notice that both designs do not fully take the 1 convolutions (also called pointwise convolutions in [12]) into account, which require considerable complex-ity.

  Convolutions, Separable, Depthwise, Depthwise separable convolutions