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

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

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

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.

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

Image Restoration Using Very Deep Convolutional Encoder ...

The proposed framework mainly contains a chain of convolutional layers and symmetric decon-volutional layers, as shown in Figure 1. We term our method “RED-Net”—very deep Residual Encoder-Decoder Networks. 2.1 Architecture The framework is fully convolutional and deconvolutional. Rectification layers are added after each

  Using, Decoder, Deep, Restoration, Very, Convolutional, Restoration using very deep convolutional

Graph Convolutional Matrix Completion

The decoder model is a pairwise decoder Aˇ = д(Z), which takes pairs of node embeddings (zi,zj)and predicts entries Aˇ ... Graph Convolutional Matrix Completion KDD’18 Deep Learning Day, August 2018, London, UK.

  Decoder, Matrix, Deep, Completion, Graph, Convolutional, Graph convolutional matrix completion

Abstract - arXiv

Convolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a

  Decoder, Deep, Convolutional

Stacked Convolutional Auto-Encoders for Hierarchical ...

Deep architectures can be trained in an unsupervised layer-wise fashion, and later fine-tuned by back-propagationto be-come classifiers [9]. Unsupervised initializations tend to avoid local minima and increase the network’s performance stability [6]. Most methods are based on the encoder-decoder paradigm, e.g., [20]. The in-

  Decoder, Deep, Convolutional

Image Colorization with Deep Convolutional Neural …

Image Colorization with Deep Convolutional Neural Networks Jeff Hwang jhwang89@stanford.edu You Zhou youzhou@stanford.edu Abstract We present a convolutional-neural-network-based sys-tem that faithfully colorizes black and white photographic images without direct human assistance. We explore var-ious network architectures, objectives, color ...

  Network, With, Image, Deep, Neural, Convolutional, Colorization, Image colorization with deep convolutional neural, Image colorization with deep convolutional neural networks

Convolutional LSTM Network: A Machine Learning Approach ...

Convolutional LSTM Network: A Machine Learning ... According to the philosophy underlying the deep learning approach, if we have a reasonable end-to-end model and sufficient data for training it, we are close to solving the ... The pioneering LSTM encoder-decoder framework proposed in [23] provides a

  Decoder, Deep, Convolutional

Long-Term Recurrent Convolutional Networks for Visual ...

deep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image de-scription and retrieval problems, and video narration ...

  Deep, Convolutional

Deep Convolutional Dictionary Learning for Image Denoising

Deep Convolutional Dictionary Learning for Image Denoising Hongyi Zhenga,b,* Hongwei Yonga,b,* Lei Zhanga,b,† aThe Hong Kong Polytechnic University bDAMO Academy, Alibaba Group {cshzheng,cshyong,cslzhang}@comp.polyu.edu.hk Abstract Inspired by the great success of deep neural net-

  Deep, Convolutional, Deep convolutional

arXiv:1706.02216v4 [cs.SI] 10 Sep 2018

Graph convolutional networks. In recent years, several convolutional neural network architectures for learning over graphs have been proposed (e.g., [4, 9, 8, 17, 24]). The majority of these methods do not scale to large graphs or are designed for whole-graph classification (or both) [4, 9, 8, 24].

  Convolutional