Learning Convolutional Neural Networks for Graphs

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

Densely Connected Convolutional Networks - arXiv

embrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with Llayers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For

  Network, Convolutional, Convolutional networks, Densenet, Convos, Lutional

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

tion networks, we propose a training scheme that alternates between fine-tuning for the region proposal task and then fine-tuning for object detection, while keeping the proposals fixed. This scheme converges quickly and produces a unified network with convo-lutional features that are shared between both tasks.1

  Network, Faster, Convos, Faster r cnn, Lutional

Optimizing FPGA-based Accelerator Design for Deep ...

based on traditional arti cial neural networks. The purpose of this classi er is to decide the likelihood of categories that the input (e.g. image) might belong to. ... will form the set of input feature maps for the next convo-lutional layer. The pseudo code of a convolutional layer can be written as that in Code 1. Figure 1: Graph of a ...

  Network, Convos, Lutional

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

Also, we show that on 3D-videos, 4D spatio-temporal convo-lutional neural networks are robust to noise and outperform 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,

  Network, Convos, Lutional

Densely Connected Convolutional Networks

embrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For

  Network, Convolutional, Convolutional networks, Convos, Lutional

Spatial Pyramid Pooling in Deep Convolutional Networks …

The convo-lutional layers operate in a sliding-window manner and output feature maps which represent the spatial arrangement of the activations (Figure2). In fact, con- ... networks [3] cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses only a single window size. Multi-level pooling has been

  Network, Pyramid, Spatial, Convolutional, Convolutional networks, Pooling, Convos, Lutional, Spatial pyramid pooling

Deconvolutional Networks - matthewzeiler

Our proposed model is similar in spirit to the Convo-lutional Networks of LeCun et al. [13], but quite different in operation. Convolutional networks are a bottom-up (a) (b) Figure 1. (a): “Tokens” from Fig. 2-4 of Vision by D. Marr [18]. These idealized local groupings are proposed as an intermediate

  Network, Convolutional, Convolutional networks, Convos, Convolu tional networks, Lutional