Wasserstein Generative Adversarial Networks

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

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

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

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

Variational Inference with Normalizing Flows

provement in performance (Mnih & Gregor,2014). There is also a large body of evidence that describes the detri-mental effect of limited posterior approximations.Turner & Sahani(2011) provide an exposition of two commonly experienced problems. The first is the widely-observed problem of under-estimation of the variance of the poste-

  Inference, Estimation, Gregor, Variational, Variational inference

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

Abstract. The Super-Resolution Generative Adversarial Network (SR-GAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SR-

  Adversarial, Generative, Generative adversarial

CartoonGAN: Generative Adversarial Networks for Photo ...

toonGAN, a generative adversarial network (GAN) frame-work for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of

  Adversarial, Generative, Generative adversarial

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 ...

  Adversarial, Generative, Generative adversarial

ffirstname.lastnameg@tue.mpg.de arXiv:2011.12100v2 [cs.CV ...

GAN-based Image Synthesis: Generative Adversarial Networks (GANs) [24] have been shown to allow for pho-torealistic image synthesis at resolutions of 10242 pixels and beyond [6,14,15,39,40]. To gain better control over the synthesis process, many works investigate how factors of variation can be disentangled without explicit supervi-sion.

  Adversarial, Generative, Generative adversarial

Conditional Image Synthesis with Auxiliary Classifier GANs

Generative adversarial networks (GANs) offer a distinct and promising approach that focuses on a game-theoretic formulation for training an image synthesis model (Good-fellow et al.,2014). Recent work has shown that GANs can produce convincing image samples on datasets with low variability and low resolution (Denton et al.,2015;Radford et al ...

  Adversarial, Generative, Conditional, Generative adversarial

Lecture 13: Generative Models

Generative models Explicit density Implicit density Direct Tractable density Approximate density Markov Chain Variational Markov Chain Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017. Today: discuss 3 most popular types of generative models today

  Adversarial, Generative, Generative adversarial

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

detecting adversarial samples in a sense, but do not utilize the Mahalanobis distance-based metric, i.e., they only utilize the Euclidean distance in their scores. In this paper, we show that Mahalanobis distance is significantly more effective than the Euclidean distance in various tasks. Experimental supports for generative classifiers.

  Adversarial, Generative

“Deep Fakes” using Generative Adversarial Networks (GAN)

Generative Adversarial Networks (GAN) The basic module for generating fake images is a GAN. A block diagram of a typical GAN network is shown in Fig-ure2. A GAN network is consisted of a generator and a discriminator. During the training period, we use a data set

  Network, Using, Deep, Efka, Adversarial, Generative, Generative adversarial, Deep fakes using generative adversarial networks

Deep Learning on Graphs - Michigan State University

6.3 Graph Adversarial Defenses 151 6.3.1 Graph Adversarial Training 152 6.3.2 Graph Purification 154 6.3.3 Graph Attention 155 6.3.4 Graph Structure Learning 159 6.4 Conclusion 160 6.5 Further Reading 160 7 Scalable Graph Neural Networks 162 7.1 Introduction 162 7.2 Node-wise Sampling Methods 166 7.3 Layer-wise Sampling Methods 168

  Learning, Deep, Graph, Adversarial, Deep learning on graphs