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

AS AND A-LEVEL PHYSICS

specific to their particular science, and that are particularly valued in higher education. Students will receive a separate grade for their practical skills (a pass/fail grade). These reforms should place experimentation and practical skills at the heart of science teaching, where they should be.

  Their

Essential Graphs for Microeconomics - Weebly

run graphs as the lead in to the understanding of the long-run equilibrium in competitive firms and its meaning. Always begin with this lesson by showing why the Demand curve and the MR curve are the same since a perfectly competitive seller earns the price each time another unit is sold. MC P ATC P=D=MR=AR P e Q e Q Q P MR ATC MC P Q MR=MC

  Graph

Chapter 2: Frequency Distributions and Graphs (or making ...

Ch2: Frequency Distributions and Graphs Santorico -Page 29 Titanic Example: Survival status and sex are qualitative variables. The following tables give their categorical frequency distributions. Survival Status We’ll come back for graphs which can include a pie graph, bar chart or Pareto chart. Example: Areas of study for students in our class

  Their, Graph

Lecture 4: Matching Algorithms for Bipartite Graphs

Main idea for the algorithm that nds a maximum matching on bipartite graphs comes from the following fact: Given some matching M and an augmenting path P, M 0= M P is a matching with jM j= jMj+1. Here, ’ ’ denotes the symmetric di erence set operation (everything that belongs to both sets individually, but doesn’t belong to their ...

  Their, Matching, Algorithm, Graph, Bipartite, Matching algorithms for bipartite graphs

LEARNWELL OY - Chinese University of Hong Kong

Most graphs will have two trends, or there will be two graphs with a trend in each. You could tell about the two trends in two separate paragraphs. Make sure you have identified the trends in the graph. Introduction • First sentence: Describe the graph. …

  Graph, Learnwell oy, Learnwell

Inductive Representation Learning on Large Graphs

graphs (e.g., citation data with text attributes, biological data with functional/molecular markers), our approach can also make use of structural features that are present in all graphs (e.g., node degrees). Thus, our algorithm can also be applied to graphs without node features.

  Inductive, Graph

Querying Knowledge Graphs by Example Entity Tuples

graphs in the lattice can be prohibitively expensive. For efficient query processing (Sec.5), GQBE employs a top-k lattice exploration algorithm that only partially evaluates the lattice nodes in the order of their corresponding query graphs’ upper-bound scores. We summarize the contributions of this paper as follows:

  Their, Graph