Patrick Emami
Updates on my machine learning research, summaries of papers, and blog posts

Deep Reinforcement Learning

Recurrent Environment Simulators
// Chiappa, et al., 2017

Variational Information Maximizing Exploration
// Houthooft, et al., 2016 ~ OpenAI

Deep Exploration via Bootstrapped DQN
// Osband, et al., 2016

Neural Fitted Q Iteration
// Riedmiller, M. 2005

Prioritized Experience Replay
// Schaul, Quan, Antonoglou, Silver, 2016

Incentivizing Exploration in Reinforcement Learning with Deep Predictive Models
// Bradly C. Stadie, Sergey Levine, Pieter Abbeel, 2015

Computer Vision

Learning From Demonstrations in the Wild
// Behbahani et al., 2018

Pixel Recursive Super Resolution
// Dahl, Norouzi, Shlens, 2017

Reinforcement Learning Theory

RUDDER: Return Decomposition for Delayed Rewards
// Arjona-Medina, Gillhofer, et al., 2018

Cooperative Inverse Reinforcement Learning
// Hadfield-Menell, et al., 2016

Generative Adversarial Networks

Natural Language Processing

A Neural Probabilistic Language Model
// Bengio, et al., 2003

Deep Learning Theory

What Can Neural Networks Reason About?
// Xu, Li, Zhang, Du, Kawarabayashi, Jegelka, 2020

Convexified Convolutional Neural Networks
// Zhang, Liang, Wainwright, 2016

Artificial General Intelligence

General Machine Learning

The Markov Chain Monte Carlo Revolution
// Persi Diaconis, 2009

Topology and Data
// Gunnar Carlsson, 2009

Intention-Aware Risk Estimation: Field Results
// Lefevre, Vasquez, Laugier, Ibanez-Guzman, 2015