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Improved Techniques for Training GANs
TLDR
This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes. Expand
Improved Variational Inference with Inverse Autoregressive Flow
TLDR
A new type of normalizing flow, inverse autoregressive flow (IAF), is proposed that, in contrast to earlier published flows, scales well to high-dimensional latent spaces and significantly improves upon diagonal Gaussian approximate posteriors. Expand
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
TLDR
This work explores the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients, and highlights several advantages of ES as a blackbox optimization technique. Expand
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
TLDR
A reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction is presented, improving the conditioning of the optimization problem and speeding up convergence of stochastic gradient descent. Expand
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
TLDR
This work discusses the implementation of PixelCNNs, a recently proposed class of powerful generative models with tractable likelihood that contains a number of modifications to the original model that both simplify its structure and improve its performance. Expand
Variational Lossy Autoencoder
TLDR
This paper presents a simple but principled method to learn global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN with greatly improve generative modeling performance of VAEs. Expand
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
TLDR
A new synthesis of variational inference and Monte Carlo methods where one or more steps of MCMC is incorporated into the authors' variational approximation, resulting in a rich class of inference algorithms bridging the gap between variational methods and MCMC. Expand
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression
TLDR
A general algorithm for approximating nonstandard Bayesian posterior distributions that minimizes the Kullback-Leibler divergence of an approximating distribution to the intractable posterior distribu- tion. Expand
Dota 2 with Large Scale Deep Reinforcement Learning
TLDR
By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task. Expand
Improving GANs Using Optimal Transport
TLDR
Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution, resulting in a highly discriminative distance function with unbiased mini-batch gradients is presented. Expand
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