Search Results for author: Yan Duan

Found 21 papers, 15 papers with code

Data mining, dashboard and statistical analysis: a powerful framework for the chemical design of molecular nanomagnets

1 code implementation4 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

Spectroscopic analysis of vibronic relaxation pathways in molecular spin qubit $[$Ho(W$_5$O$_{18}$)$_2]^{9-}$: sparse spectra are key

no code implementations17 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

Variable Skipping for Autoregressive Range Density Estimation

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.

Data Augmentation Density Estimation

NeuroCard: One Cardinality Estimator for All Tables

1 code implementation15 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.

Evaluating Protein Transfer Learning with TAPE

5 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.

BIG-bench Machine Learning Representation Learning +1

Deep Unsupervised Cardinality Estimation

1 code implementation10 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.

Density Estimation

The Importance of Sampling inMeta-Reinforcement Learning

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.

Meta Reinforcement Learning reinforcement-learning

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

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.

Policy Gradient Methods reinforcement-learning

Model-Ensemble Trust-Region Policy Optimization

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.

Continuous Control Model-based Reinforcement Learning +1

Stochastic Neural Networks for Hierarchical Reinforcement Learning

1 code implementation10 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

One-Shot Imitation Learning

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.

Feature Engineering Imitation Learning +1

Adversarial Attacks on Neural Network Policies

no code implementations8 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.


#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

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.

Atari Games Continuous Control +1

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

16 code implementations9 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.


Variational Lossy Autoencoder

no code implementations8 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.

Density Estimation Image Generation +1

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

35 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.

Image Generation Representation Learning +2

VIME: Variational Information Maximizing Exploration

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.

Continuous Control reinforcement-learning +1

Benchmarking Deep Reinforcement Learning for Continuous Control

15 code implementations22 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.

Action Triplet Recognition Atari Games +2

Deep Spatial Autoencoders for Visuomotor Learning

1 code implementation21 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.


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