Deep Gaussian Processes

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.

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

Variational Inference with Normalizing Flows

provement in performance (Mnih & Gregor,2014). There is also a large body of evidence that describes the detri-mental effect of limited posterior approximations.Turner & Sahani(2011) provide an exposition of two commonly experienced problems. The first is the widely-observed problem of under-estimation of the variance of the poste-

  Inference, Estimation, Gregor, Variational, Variational inference

GAUSSIAN 09W TUTORIAL - McGill University

GAUSSIAN 09W TUTORIAL AN INTRODUCTION TO COMPUTATIONAL CHEMISTRY USING G09W AND AVOGADRO SOFTWARE Anna Tomberg anna.tomberg@mail.mcgill.com This is a quick tutorial that will help you to make your way through the first steps of computational chemistry using Gaussian 09W software (G09).

  Gaussian

7 Gaussian Elimination and LU Factorization

with row k. This process is referred to as partial (row) pivoting. Partial column pivoting and complete (row and column) pivoting are also possible, but not very popular. Example Consider again the matrix A = 1 1 1 2 2+ε 5 4 6 8 The largest element in the first column is the 4 in the (3,1) position. This is our first

  Process, Gaussian

3. The Gaussian kernel

The Gaussian kernel is defined in 1-D, 2D and N-D respectively as ... process of observation s can never become zero. For, this would imply making an observation through an infinitesimally small aperture, which is impossible. The factor of 2 in the exponent is a matter of convention,

  Process, Kernel, Gaussian, Gaussian kernel

An Intuitive Tutorial to Gaussian Processes Regression

An Intuitive Tutorial to Gaussian Processes Regression 3 Gaussian vector x2 = [x1 2, x 2 2,. . ., x n 2] in the same coordinates at Y = 1 shown in Fig.3. Keep in mind that either x 1 or x2 is a uni-variate normal distribution shown in Fig.2. 0.0 0.2 0.4 0.6 0.8 1.0

  Processes, Tutorials, Regression, Gaussian, Intuitive, Intuitive tutorial to gaussian processes regression

Gaussian mixture models and the EM algorithm

A Gaussian mixture model (GMM) is useful for modeling data that comes from one of several groups: the groups might be di erent from each other, but data points within the same group can be well-modeled by a Gaussian distribution. ... the other will follow almost the same process.

  Process, Gaussian

Gaussian Processes in Machine Learning

A Gaussian Process is a collection of random variables, any finite number of which have (consistent) joint Gaussian distributions. A Gaussian process is fully specified by its mean function m(x) and covariance function k(x,x0). This is a …

  Process, Gaussian, Gaussian process

Gaussian Elimination and Back Substitution

The process of eliminating variables from the equations, or, equivalently, zeroing entries of the corresponding matrix, in order to reduce the system to upper …

  Process, Gaussian

Appropriate Gaussian quadrature formulae for triangles

of Gaussian points and overcomes all the drawbacks in view of accuracy and e ciency for the numerical evaluation of the ... averaging process in engineering. In probabilistic estimations and in spatially discretized approxima-tions, e.g., nite and boundary-element methods, evaluation of integrals over arbitrary-shaped domain ...

  Process, Gaussian