Actor-Attention-Critic for Multi-Agent Reinforcement …

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

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

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

Algorithms for Reinforcement Learning

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further,

  Learning, Reinforcement, Reinforcement learning

DRN: A Deep Reinforcement Learning Framework for News ...

simultaneously. Some recent attempts using reinforcement learn-ing in recommendation either do not model the future reward explicitly (MAB-based works [23, 43]), or use discrete user log to represent state and hence can not be scaled to large systems (MDP-based works [35, 36]). In contrast, our framework uses a DQN structure and can easily ...

  Framework, Learning, Learn, Deep, News, Reinforcement, Re inforcement learning, Deep reinforcement learning framework for news

Mastering the Game of Go without Human Knowledge

In contrast, reinforcement learn-ing systems are trained from their own experience, in principle allowing them to exceed human capabilities, and to operate in domains where human expertise is lacking. Recently, there has been rapid progress towards this goal, using deep neural networks trained by reinforcement learning.

  Learning, Learn, Reinforcement, Reinforcement learning, Re inforcement learning

Foundations of Game-Based Learning

ing how video games shape cognitive development and learning. In one of the first books on the psychology of video games, Loftus and Loftus (1983) focused on players’ moti- ... reinforcement schedule—the reinforcement schedule that produces the …

  Reinforcement

Mastering the game of Go without human knowledge

uation. The policy network was trained initially by supervised learn ­ ing to accurately predict human expert moves, and was subsequently refined by policy­gradient reinforcement learning. The value network was trained to predict the winner of games played by the policy net ­ work against itself. Once trained, these networks were combined with

  Human, Without, Learning, Learn, Knowledge, Reinforcement, Reinforcement learning, Of go without human knowledge

I. AUDIO-LINGUAL METHOD: Introduction by Diane Larsen …

Positive reinforcement helps students to develop correct habits. Video Presentation: The first method we will observe is the Audio-Lingual Method or ALM. It is a method with ... Language learn-ing is seen to be a process of habit formation. The more often the students repeat something, the stronger

  Audio, Learn, Reinforcement, Lingual, Audio lingual

Learned Helplessness: Theory and Evidence

Jun 30, 1975 · ing! and outcomes to which organisms are sensitive in terms of the _ conditional prob-ability of an outcome or reinforcer following a response />(RF/R), which can have values ranging from 0 to 1.0. At 1.0, every re-sponse produces a reinforcer or outcome (continuous reinforcement). At- 0, a re-sponse never produces a reinforcer (extinc-tion).

  Learned, Reinforcement, Learned helplessness, Helplessness

TWELVE STEP FACILITATION THERAPY MANUAL

ing research. Enoch Gordis, M.D. Director National Institute on Alcohol Abuse and Alcoholism. ix Preface ... and reinforcement for AA participation, introduction and explication of the week’s theme, and setting goals for AA participation for the next week. Material introduced during treatment sessions is complemented

  Step, Facilitation, Reinforcement, Step facilitation