1 code implementation • 4 Mar 2021 • Yan Duan, Joana T. Coutinho, Lorena E. Rosaleny, Salvador Cardona-Serra, José J. Baldoví, Alejandro Gaita-Ariño
Three decades of research in molecular nanomagnets have raised their magnetic memories from liquid helium to liquid nitrogen temperature thanks to a wise choice of the magnetic ion and coordination environment.
Mesoscale and Nanoscale Physics
no code implementations • 17 Feb 2021 • Avery L. Blockmon, Aman Ullah, Kendall D. Hughey, Yan Duan, Kenneth R. O'Neal, Mykhaylo Ozerov, José J. Baldoví, Juan Aragó, Alejandro Gaita-Ariño, Eugenio Coronado, Janice L. Musfeldt
Molecular vibrations play a key role in magnetic relaxation processes of molecular spin qubits as they couple to spin states, leading to the loss of quantum information.
Mesoscale and Nanoscale Physics
1 code implementation • ICML 2020 • Eric Liang, Zongheng Yang, Ion Stoica, Pieter Abbeel, Yan Duan, Xi Chen
In this paper, we explore a technique, variable skipping, for accelerating range density estimation over deep autoregressive models.
1 code implementation • 15 Jun 2020 • Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, Ion Stoica
Query optimizers rely on accurate cardinality estimates to produce good execution plans.
6 code implementations • NeurIPS 2019 • Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, Yun S. Song
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.
1 code implementation • 10 May 2019 • Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M. Hellerstein, Sanjay Krishnan, Ion Stoica
To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of dimensions or more.
4 code implementations • ICLR 2019 • Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Ranked #13 on
Image Generation
on ImageNet 32x32
(bpd metric)
no code implementations • NeurIPS 2018 • Bradly Stadie, Ge Yang, Rein Houthooft, Peter Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning.
no code implementations • ICLR 2018 • Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M. Bayen, Sham Kakade, Igor Mordatch, Pieter Abbeel
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
7 code implementations • ICLR 2018 • Bradly C. Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever
We consider the problem of exploration in meta reinforcement learning.
2 code implementations • ICLR 2018 • Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel
In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.
2 code implementations • 10 Apr 2017 • Carlos Florensa, Yan Duan, Pieter Abbeel
Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
no code implementations • NeurIPS 2017 • Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba
A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.
1 code implementation • 8 Feb 2017 • Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, Pieter Abbeel
Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification.
3 code implementations • NeurIPS 2017 • Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
18 code implementations • 9 Nov 2016 • Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel
The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP.
no code implementations • 8 Nov 2016 • Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.
37 code implementations • NeurIPS 2016 • Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
Ranked #3 on
Image Generation
on Stanford Dogs
2 code implementations • NeurIPS 2016 • Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.
15 code implementations • 22 Apr 2016 • Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Ranked #1 on
Continuous Control
on Inverted Pendulum
1 code implementation • 21 Sep 2015 • Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel
Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models.