Convolutional Neural Networks

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

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

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.

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

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

Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015

segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% rela-tive improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image. 1. Introduction Convolutional networks are driving advances in recog-nition.

  Network, Convolutional, Convolutional networks

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

3 Convolutional neural networks Since 2012, one of the most important results in Deep Learning is the use of convolutional neural networks to obtain a remarkable improvement in object recognition for ImageNet [25]. In the following sections, I will discuss this powerful architecture in detail. 3.1 Using local networks for high dimensional inputs

  Neural, Convolutional, Convolutional neural

Bag of Tricks for Image Classification with Convolutional ...

The template of training a neural network with mini-batch stochastic gradient descent is shown in Algorithm 1. In each iteration, we randomly sample b images to com-pute the gradients and then update the network parameters. It stops after K passes through the dataset. All functions and hyper-parameters in Algorithm 1 can be implemented

  Network, Neural network, Neural, Convolutional

4D Spatio-Temporal ConvNets: Minkowski Convolutional ...

the 3D convolutional neural network. 1. Introduction In this work, we are interested in 3D-video perception. A 3D-video is a temporal sequence of 3D scans such as a video from a depth camera, a sequence of LIDAR scans, or a multiple MRI scans of the same object or a body part (Fig. 1). As LIDAR scanners and depth cameras become

  Network, Neural, Convolutional, Convolutional neural networks

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.

  Network, Neural network, Neural, Convolutional, Convolutional neural

Convolutional Neural Network - 國立臺灣大學

Convolutional Neural Network (CNN) Network Architecture designed for Image 1. Image Classification Model ... Benefit of Convolutional Layer Fully Connected Layer •Some patterns are much smaller than the whole image. Receptive Field …

  Network, Neural, Convolutional, Convolutional neural networks

A Convolutional Recurrent Neural Network for Real-Time ...

A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement Ke Tan 1, DeLiang Wang 1 ;2 1 Department of Computer Science and Engineering, The Ohio State University, USA 2 Center for Cognitive and Brain Sciences, The Ohio State University, USA tan.650@osu.edu, wang.77@osu.edu Abstract Many real-world applications of speech …

  Network, Neural, Convolutional, Recurrent, A convolutional recurrent neural network for

ISAAC: A Convolutional Neural Network Accelerator with In ...

ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars Ali Shafiee ∗, Anirban Nag , Naveen Muralimanohar†, Rajeev Balasubramonian∗, John Paul Strachan †, Miao Hu , R. Stanley Williams†, Vivek Srikumar∗ ∗School of Computing, University of Utah, Salt Lake City, Utah, USA Email: {shafiee, anirban, rajeev, svivek}@cs.utah.edu

  Network, Neural, Convolutional, Convolutional neural networks