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.

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

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

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

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

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

Learning Transferable Features with Deep Adaptation Networks

deep networks, resulting in statistically unboundedrisk for target tasks (Mansour et al., 2009; Ben-David et al., 2010). Our work is primarily motivated by Yosinski et al. (2014), which comprehensively explores feature transferability of deep convolutional neural networks. The method focuses on a different scenario where the learning tasks are ...

  Network, Deep, Deep networks

“Deep Fakes” using Generative Adversarial Networks (GAN)

two GAN networks, and other than the loss in the tradi-tional GAN network, it also included a cycle-consistency loss to ensure any input is mapped to a relatively reasonable output. 2. Physical and Mathematical framework The framework we used in this project is a Cycle-GAN based on deep convolutional GANs. 2.1. Generative Adversarial Networks (GAN)

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

Spatio-Temporal Graph Convolutional Networks: A Deep ...

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for TrafÞc Forecasting Bing Yu! 1, Haoteng Yin! 2,3, Zhanxing Zhu 3,4 1 School of Mathematical Sciences, Peking University, Beijing, China 2 Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 3 Center for Data Science, Peking University, Beijing, China

  Network, Deep, Graph, Convolutional, Temporal, Positas, Spatio temporal graph convolutional networks, A deep

Sequence to Sequence Learning with Neural Networks

Deep Neural Networks (DNNs) are extremely powerful machine learning models that achieve ex-cellent performanceon difficult problems such as speech rec ognition[13, 7] and visual object recog-nition [19, 6, 21, 20]. DNNs are powerful because they can perform arbitrary parallel computation for a modest number of steps.

  Network, Deep

Multifaceted Feature Visualization: Uncovering the ...

We can better understand deep neural networks by identifying which features each of their neu-rons have learned to detect. To do so, researchers have created Deep Visualization techniques in-cluding activation maximization, which synthet-ically generates inputs (e.g. images) that maxi-mally activate each neuron. A limitation of cur-

  Network, Deep