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.

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

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

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

A Tutorial on Deep Learning Part 2: Autoencoders ...

Translational invariance via convolutional neural networks which require modi cations in the network architecture, Variable-sized sequence prediction via recurrent neural networks which require modi cations in the network architecture. The exibility of neural networks is a very powerful property. In many cases, these changes lead to great

  Network, Neural, Convolutional, Recurrent, Convolutional neural, Recurrent neural

14. Applications of Convolutional Neural Networks

Recurrent architecture [25] for convolutional neural network suggests a sequential series of networks sharing the same set of parameters. The network automatically learns to smooth its own predicted labels. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. A simple and scalable detection

  Network, Neural, Convolutional, Recurrent, Convolutional neural networks, Convolutional neural

Densely Connected Convolutional Networks - arXiv

to (unrolled) recurrent neural networks [21], but the num-ber of parameters of ResNets is substantially larger because each layer has its own weights. Our proposed DenseNet ar-chitecture explicitly differentiates between information that is added to the network and information that is preserved.

  Network, Neural, Convolutional, Recurrent, Densenet, Recurrent neural

Look Closer to See Better: Recurrent Attention ...

In this section, we will introduce the proposed recurrent attention convolutional neural network (RA-CNN) for fine-grained image recognition. We consider the network with three scales as an example in Figure 2, and more finer s-cales can be stacked in a similar way. The inputs are recur-rent from full-size images in a1 to fine-grained ...

  Network, Entr, Neural, Convolutional, Recurrent, Convolutional neural networks, Curre, Recur rent

Abstract - arXiv

propose a recurrent convolutional neural network to model the spatial relationships but the model only predicts one frame ahead and the size of the convolutional kernel used for state-to-state tran-sition is restricted to 1. Their work is followed up …

  Network, Neural, Convolutional, Recurrent, Recurrent convolutional neural network

A Primer on Neural Network Models for Natural Language ...

2. Neural Network Architectures Neural networks are powerful learning models. We will discuss two kinds of neural network architectures, that can be mixed and matched { feed-forward networks and Recurrent / Recursive networks. Feed-forward networks include networks with fully connected layers,

  Network, Neural network, Neural, Recurrent

Convolutional Neural Networks for Visual Recognition

Choy et al., 3D-R2N2: Recurrent Reconstruction Neural Network (2016) Mandlekar and Xu et al., Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations (2020) Xu et al., PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation (2018) 3D Vision & Robotic Vision Wang et al., 6-PACK: Category-level 6D Pose Tracker with

  Network, Visual, Recognition, Neural network, Neural, Convolutional, Recurrent, Convolutional neural networks for visual recognition

Lecture 10: Recurrent Neural Networks

Recurrent Neural Network x RNN y We can process a sequence of vectors x by applying a recurrence formula at every time step: Notice: the same function and the same set of parameters are used at every time step. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - …

  Network, Neural, Recurrent, Recurrent neural, Recurrent neural networks

Social-STGCNN: A Social Spatio-Temporal Graph ...

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction Abduallah Mohamed1, Kun Qian1 Mohamed Elhoseiny2,3, **, Christian Claudel1, ** 1The University of Texas at Austin 2KAUST 3Stanford University {abduallah.mohamed,kunqian,christian.claudel}@utexas.edu, mohamed.elhoseiny@kaust.edu.sa

  Network, Neural, Convolutional, Convolutional neural networks