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

Dropout: A Simple Way to Prevent Neural Networks from …

Dropout: A Simple Way to Prevent Neural Networks from Over tting Nitish Srivastava nitish@cs.toronto.edu Geo rey Hinton hinton@cs.toronto.edu Alex Krizhevsky kriz@cs.toronto.edu Ilya Sutskever ilya@cs.toronto.edu Ruslan Salakhutdinov rsalakhu@cs.toronto.edu Department of Computer Science University of Toronto 10 Kings …

  Form, Network, Simple, Prevent, Neural, Simple way to prevent neural networks from

Lipschitz regularity of deep neural networks: analysis and ...

unrealistic upper bound. In order to prevent this, we propose an improved algorithm called SeqLip in the case of sequential neural networks, and show in Section 7 that SeqLip may significantly im-prove on AutoLip. Finally we discuss the different algorithms on the AlexNet [1] neural network for computer vision using the proposed algorithms. 1

  Analysis, Network, Deep, Prevent, Neural network, Neural, To prevent, Analysis and, Lipschitz regularity of deep neural networks, Lipschitz, Regularity

Generating Sequences With Recurrent Neural Networks - …

To prevent this, all the experiments in this pa-per clipped the derivative of the loss with respect to the network inputs to the LSTM layers (before the sigmoid and tanhfunctions are applied) to lie within a prede ned range1. 3 Text Prediction Text data is discrete, and is typically presented to neural networks using ‘one-= =)

  Network, With, Sequence, Prevent, Generating, Neural network, Neural, Recurrent, To prevent, Generating sequences with recurrent neural networks

Load Forecasting: Methods & Techniques - SAARC Energy

Too low forecasts prevent optimum economic growth ... •Expert systems approach (like Neural networks, Fuzzy logic) ... Analysis of load in terms of MW/sq.km or MU/sq.km is a convenient way of relating it to local T&D capacity needs and is often used in power delivery planning.

  Network, Prevent, Neural network, Neural

Siamese Neural Networks for One-shot Image Recognition

Siamese Neural Networks for One-shot Image Recognition Figure 3. A simple 2 hidden layer siamese network for binary classification with logistic prediction p. The structure of the net-work is replicated across the top and bottom sections to form twin networks, with shared weight matrices at each layer.

  Network, Neural network, Neural

Winter Quarter 2019 Stanford University

3 Convolutional Neural Networks 28 4 Adversarial Attacks 10 5 Loss comparisons 23 6 The Optimizer 20 Total 117 The exam contains29pages including this cover page. • This exam is closed book i.e. no laptops, notes, textbooks, etc. during the exam. However, you may use one A4 sheet (front and back) of notes as reference.

  Network, Neural network, Neural

Wide Residual Networks arXiv:1605.07146v4 [cs.CV] 14 Jun ...

a much more effective way of improving performance of residual networks compared to in-creasing their depth. In particular, we present wider deep residual networks that significantly improve over [13], having 50 times less layers and being more than 2 times faster. We call the resulting network architectures wide residual networks.

  Network

End to End Learning for Self-Driving Cars - Nvidia

Autonomous Land Vehicle in a Neural Network (ALVINN) system. It demonstrated that an end-to-end trained neural network can indeed steer a car on public roads. Our work differs in that 25 years of advances let us apply far more data and computational power to the task. In addition, our experience with CNNs lets us make use of this powerful ...

  Driving, Neural, End to end

ImageNet Classification with Deep Convolutional Neural ...

neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution ...

  With, Classification, Deep, Neural, Convolutional, Imagenet, Imagenet classification with deep convolutional neural