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.

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

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

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

An introduction to neural networks for beginners

Part 1 – Introduction to neural networks 1.1 WHAT ARE ARTIFICIAL NEURAL NETWORKS? Artificial neural networks (ANNs) are software implementations of the neuronal structure of our brains. We don’t need to talk about the complex biology of our brain structures, but suffice to say, the brain contains neurons which are kind of like organic switches.

  Introduction, Network, Beginner, Neural network, Neural, An introduction to neural networks for beginners

Lecture 12 Introduction to Neural Networks

networks, though we will (hopefully) have a chance to talk about recurrent neural networks (RNNs) that allow for loops in the network. The one-directional nature of feed-forward networks is probably the biggest difference between artificial neural networks and their biological equivalent. 18/37

  Introduction, Network, Neural network, Neural

Neural Networks and Statistical Models

neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis. Introduction Neural networks are a wide class of flexible nonlinear regression and discriminant models, data reduction models, and nonlinear dynamical systems. They consist of an often large number of

  Introduction, Network, Model, Neural network, Neural, Introduction neural networks

An Introduction to Neural Networks - Iowa State University

An Introduction to Neural Networks Vincent Cheung Kevin Cannons Signal & Data Compression Laboratory Electrical & Computer Engineering University of Manitoba Winnipeg, Manitoba, Canada Advisor: Dr. W. Kinsner

  Introduction, Network, Neural, An introduction to neural networks

SHIWEN WU, FEI SUN, WENTAO ZHANG, arXiv:2011.02260v2 …

Graph Neural Networks in Recommender Systems: A Survey SHIWEN WU, Peking University FEI SUN, Alibaba Group WENTAO ZHANG, Peking University ... 1 INTRODUCTION With the rapid development of e-commerce and social media platforms, recommender systems have become indispensable tools for many businesses [13, 145, 153]. They can be recognized as

  Introduction, Network, Neural network, Neural

arXiv:1910.03151v4 [cs.CV] 7 Apr 2020

1. Introduction Deep convolutional neural networks (CNNs) have been widely used in computer vision community, and have Qinghua Hu is the corresponding author. Email: fqlwang, wubanggu, huqinghuag@tju.edu.cn. The work was sup-ported by the National Natural Science Foundation of China (Grant No.

  Introduction, Network, Neural network, Neural

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

networks to extract spatial and temporal features from the in-put jointly. Moreover, within narrow constraints or even com-plete absence of spatial attributes, the representative ability of these networks would be hindered seriously. To take full advantage of spatial features, some researchers use convolutional neural network (CNN) to capture ...

  Network, Graph, Neural, Convolutional, Temporal, Positas, Spatio temporal graph convolutional networks

Recurrent Neural Network for Text Classification with ...

The deep neural networks (DNN) based methods usually need a large-scale corpus due to the large number of parame-ters, it is hard to train a network that generalizes well with limited data. However, the costs are extremely expensive to build the large scale resources for some NLP tasks. To deal with this problem, these models often involve an un-

  Network, Texts, Neural network, Neural, Recurrent, Recurrent neural network for text

Hierarchical Attention Networks for Document Classification

Hierarchical Attention Networks for Document Classication Zichao Yang 1, Diyi Yang1, Chris Dyer 1, Xiaodong He2, Alex Smola1, Eduard Hovy 1 1Carnegie Mellon University, 2Microsoft Research, Redmond fzichaoy, diyiy, cdyer, hovy g@cs.cmu.edu xiaohe@microsoft.com alex@smola.org Abstract We propose a hierarchical attention network for document ...

  Network