research
∙
09/12/2023
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Diffusion models have revolutionized text-to-image generation with its e...
share
research
∙
03/06/2023
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Rich data and powerful machine learning models allow us to design drugs ...
research
∙
11/20/2022
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Learning new task-specific skills from a few trials is a fundamental cha...
research
∙
07/12/2022
Split Time Series into Patches: Rethinking Long-term Series Forecasting with Dateformer
Time is one of the most significant characteristics of time-series, yet ...
research
∙
06/10/2022
Is Self-Supervised Learning More Robust Than Supervised Learning?
Self-supervised contrastive learning is a powerful tool to learn visual ...
research
∙
05/15/2022
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Deep generative models have achieved tremendous success in designing nov...
research
∙
04/25/2022
Imitation Learning from Observations under Transition Model Disparity
Learning to perform tasks by leveraging a dataset of expert observations...
research
∙
03/28/2022
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
Equivariance has been a long-standing concern in various fields ranging ...
research
∙
03/20/2022
A 3D Molecule Generative Model for Structure-Based Drug Design
We study a fundamental problem in structure-based drug design – generati...
research
∙
03/02/2022
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours
Protein structure prediction is an important method for understanding ge...
research
∙
01/28/2022
Directed Weight Neural Networks for Protein Structure Representation Learning
A protein performs biological functions by folding to a particular 3D st...
research
∙
12/04/2021
Overcome Anterograde Forgetting with Cycled Memory Networks
Learning from a sequence of tasks for a lifetime is essential for an age...
research
∙
11/26/2021
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Many practical applications of reinforcement learning require agents to ...
research
∙
11/23/2021
Reviewing continual learning from the perspective of human-level intelligence
Humans' continual learning (CL) ability is closely related to Stability ...
research
∙
11/21/2021
Learning by Active Forgetting for Neural Networks
Remembering and forgetting mechanisms are two sides of the same coin in ...
research
∙
09/18/2021
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Meta-reinforcement learning (meta-RL) algorithms allow for agents to lea...
research
∙
08/20/2021
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
We present a novel semi-supervised semantic segmentation method which jo...
research
∙
07/11/2021
Coordinate-wise Control Variates for Deep Policy Gradients
The control variates (CV) method is widely used in policy gradient estim...
research
∙
06/22/2021
Off-Policy Reinforcement Learning with Delayed Rewards
We study deep reinforcement learning (RL) algorithms with delayed reward...
research
∙
03/30/2021
DAP: Detection-Aware Pre-training with Weak Supervision
This paper presents a detection-aware pre-training (DAP) approach, which...
research
∙
02/20/2021
Learning Neural Generative Dynamics for Molecular Conformation Generation
We study how to generate molecule conformations (i.e., 3D structures) fr...
research
∙
11/05/2020
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
Quality-Diversity (QD) is a concept from Neuroevolution with some intrig...
research
∙
10/29/2020
Off-Policy Interval Estimation with Lipschitz Value Iteration
Off-policy evaluation provides an essential tool for evaluating the effe...
research
∙
10/23/2020
Learning Guidance Rewards with Trajectory-space Smoothing
Long-term temporal credit assignment is an important challenge in deep r...
research
∙
09/13/2020
Efficient Competitive Self-Play Policy Optimization
Reinforcement learning from self-play has recently reported many success...
research
∙
08/28/2020
Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity
Modeling the effects of mutations on the binding affinity plays a crucia...
research
∙
07/15/2020
Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
In this paper, we propose an effective knowledge transfer framework to b...
research
∙
07/04/2020
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion
Langevin diffusion is a powerful method for nonconvex optimization, whic...
research
∙
06/12/2020
Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch
Deep reinforcement learning (RL) algorithms have achieved great success ...
research
∙
03/07/2020
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
Change detection is a basic task of remote sensing image processing. The...
research
∙
03/01/2020
Stein Variational Inference for Discrete Distributions
Gradient-based approximate inference methods, such as Stein variational ...
research
∙
02/27/2020
State-only Imitation with Transition Dynamics Mismatch
Imitation Learning (IL) is a popular paradigm for training agents to ach...
research
∙
02/21/2020
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning
In many vision-based reinforcement learning (RL) problems, the agent con...
research
∙
01/27/2020
Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification
Remote sensing image scene classification is a fundamental but challengi...
research
∙
11/09/2019
DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network
Detecting and masking cloud and cloud shadow from satellite remote sensi...
research
∙
09/07/2019
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Heterogeneous information network (HIN) embedding has gained increasing ...
research
∙
09/05/2019
√(n)-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
In this paper, we consider the problem of online learning of Markov deci...
research
∙
07/21/2019
Characterizing Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved great success in various ...
research
∙
06/22/2019
Learning Belief Representations for Imitation Learning in POMDPs
We consider the problem of imitation learning from expert demonstrations...
research
∙
06/10/2019
Exploration via Hindsight Goal Generation
Goal-oriented reinforcement learning has recently been a practical frame...
research
∙
06/08/2019
A gradual, semi-discrete approach to generative network training via explicit wasserstein minimization
This paper provides a simple procedure to fit generative networks to tar...
research
∙
05/31/2019
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning
Recent advances in deep reinforcement learning algorithms have shown gre...
research
∙
05/27/2019
Thresholding Bandit with Optimal Aggregate Regret
We consider the thresholding bandit problem, whose goal is to find arms ...
research
∙
05/20/2019
Stochastic Variance Reduction for Deep Q-learning
Recent advances in deep reinforcement learning have achieved human-level...
research
∙
04/11/2019
Knowledge Flow: Improve Upon Your Teachers
A zoo of deep nets is available these days for almost any given task, an...
research
∙
12/04/2018
Overcoming Catastrophic Forgetting by Soft Parameter Pruning
Catastrophic forgetting is a challenge issue in continual learning when ...
research
∙
12/02/2018
Anchor Box Optimization for Object Detection
In this paper, we propose a general approach to optimize anchor boxes fo...
research
∙
11/27/2018
Understanding the Importance of Single Directions via Representative Substitution
Understanding the internal representations of deep neural networks (DNNs...
research
∙
09/03/2018
emrQA: A Large Corpus for Question Answering on Electronic Medical Records
We propose a novel methodology to generate domain-specific large-scale q...
research
∙
08/01/2018
