ICML 2018

Implicit Quantile Networks for Distributional Reinforcement Learning

ICML 2018 google/dopamine

In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.

ATARI GAMES DISTRIBUTIONAL REINFORCEMENT LEARNING

Hierarchical Text Generation and Planning for Strategic Dialogue

ICML 2018 facebookresearch/end-to-end-negotiator

End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.

DECISION MAKING TEXT GENERATION

Which Training Methods for GANs do actually Converge?

ICML 2018 facebookresearch/pytorch_GAN_zoo

In this paper, we show that the requirement of absolute continuity is necessary: we describe a simple yet prototypical counterexample showing that in the more realistic case of distributions that are not absolutely continuous, unregularized GAN training is not always convergent.

Addressing Function Approximation Error in Actor-Critic Methods

ICML 2018 chainer/chainerrl

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.

Q-LEARNING

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

ICML 2018 deepmind/scalable_agent

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.

ATARI GAMES

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

ICML 2018 anishathalye/obfuscated-gradients

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.

ADVERSARIAL ATTACK ADVERSARIAL DEFENSE

Noise2Noise: Learning Image Restoration without Clean Data

ICML 2018 NVlabs/noise2noise

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

DENOISING IMAGE RESTORATION

Neural Relational Inference for Interacting Systems

ICML 2018 ethanfetaya/nri

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.

MOTION CAPTURE

Adversarially Regularized Autoencoders

ICML 2018 jakezhaojb/ARAE

This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space.

REPRESENTATION LEARNING STYLE TRANSFER