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

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

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

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

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

EUROPOL SPOTLIGHT SHADOW MONEY

the operations of vast international networks for illicit finance. They rely on offshore tax havens, complex webs of legal business structures and corruption to facilitate a variety of criminal activities including tax evasion, fraud and money laundering. The Pandora Papers is a leak of almost 12 million documents

  International, Network, International networks

CHILD PROTECTION - World Vision International

indigenous leaders and children/youth networks) that work together to prevent and respond to abuse, neglect, exploitation and other forms of violence against children. Our programmatic work and expertise at the local level gives us the credibility and evidence to advocate for stronger child protection systems on national, regional and global level.

  International, Network

node2vec: Scalable Feature Learning for Networks

networks with millions of nodes in a few hours. Overall our paper makes the following contributions: 1.We propose node2vec, an efficient scalable algorithm for feature learning in networks that efficiently optimizes a novel network-aware, neighborhood preserving objective using SGD. 2.We show how node2vec is in accordance with established u s ...

  Network, Node2vec

Communication-Efficient Learning of Deep Networks from ...

Communication-Efficient Learning of Deep Networks from Decentralized Data Federated Learning Ideal problems for federated learn-ing have the following properties: 1) Training on real-world data from mobile devices provides a distinct advantage over training on proxy data that is generally available in the data center.

  Network

Signature Verification using a 'Siamese' Time Delay Neural ...

network consists of two identical sub-networks joined at their out­ puts. During training the two sub-networks extract features from two signatures, while the joining neuron measures the distance be­ tween the two feature vectors. Verification consists of comparing an extracted feature vector ~ith a stored feature vector for the signer.

  Network, Verification, Signature, Signature verification

Faster R-CNN: Towards Real-Time Object Detection with ...

detection networks [2], Selective Search is an order of magnitude slower, at 2 seconds per image in a CPU implementation. EdgeBoxes [6] currently provides the best tradeoff between proposal quality and speed, at 0.2 seconds per image. Nevertheless, the region proposal step still consumes as much running time as the detection network.

  Network, Faster, Faster r cnn

Corporate Social Responsibility (CSR)

concept, CSR holds that organizations exist within networks of stakeholders, face the potentially conflicting demands of these stakeholders, and translate the demands into CSR objectives and policies. In some cases though, organizations attempt to change stakeholders’ expectations (Lamberg et al. 2003).

  Network