Le Song

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Ant Financial ∙ Georgia Institute of Technology ∙ Carnegie Mellon University ∙ Intel

Associate Director, Center for Machine Learning, Associate Professor at Georgia Institute of Technology

A Biased Graph Neural Network Sampler with Near-Optimal Regret

Graph neural networks (GNN) have recently emerged as a vehicle for apply...
03/01/2021 ∙ by Qingru Zhang, et al. ∙ 0 ∙ share

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Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

The design/discovery of new materials is highly non-trivial owing to the...
11/04/2020 ∙ by Rohit Batra, et al. ∙ 0 ∙ share

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DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering

Open-domain multi-hop question answering (QA) requires to retrieve multi...
09/16/2020 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ share

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Question Directed Graph Attention Network for Numerical Reasoning over Text

Numerical reasoning over texts, such as addition, subtraction, sorting a...
09/16/2020 ∙ by Kunlong Chen, et al. ∙ 8 ∙ share

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Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

Retrosynthetic planning is a critical task in organic chemistry which id...
06/29/2020 ∙ by Binghong Chen, et al. ∙ 5 ∙ share

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Understanding Deep Architectures with Reasoning Layer

Recently, there has been a surge of interest in combining deep learning ...
06/24/2020 ∙ by Xinshi Chen, et al. ∙ 0 ∙ share

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Bandit Samplers for Training Graph Neural Networks

Several sampling algorithms with variance reduction have been proposed f...
06/10/2020 ∙ by Ziqi Liu, et al. ∙ 0 ∙ share

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Learning to Stop While Learning to Predict

There is a recent surge of interest in designing deep architectures base...
06/09/2020 ∙ by Xinshi Chen, et al. ∙ 8 ∙ share

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Intention Propagation for Multi-agent Reinforcement Learning

A hallmark of an AI agent is to mimic human beings to understand and int...
04/19/2020 ∙ by Chao Qu, et al. ∙ 11 ∙ share

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Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the arch...
04/09/2020 ∙ by Weiyang Liu, et al. ∙ 7 ∙ share

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DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

Recent studies on open-domain question answering have achieved prominent...
02/28/2020 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ share

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Heterogeneous Graph Neural Networks for Malicious Account Detection

We present, GEM, the first heterogeneous graph neural network approach f...
02/27/2020 ∙ by Ziqi Liu, et al. ∙ 0 ∙ share

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RNA Secondary Structure Prediction By Learning Unrolled Algorithms

In this paper, we propose an end-to-end deep learning model, called E2Ef...
02/13/2020 ∙ by Xinshi Chen, et al. ∙ 0 ∙ share

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Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Markov Logic Networks (MLNs), which elegantly combine logic rules and pr...
01/29/2020 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ share

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Retrosynthesis Prediction with Conditional Graph Logic Network

Retrosynthesis is one of the fundamental problems in organic chemistry. ...
01/06/2020 ∙ by Hanjun Dai, et al. ∙ 6 ∙ share

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Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

Service robots should be able to operate autonomously in dynamic and dai...
11/13/2019 ∙ by Xuesong Shi, et al. ∙ 0 ∙ share

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Neural Similarity Learning

Inner product-based convolution has been the founding stone of convoluti...
10/28/2019 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Learn to Explain Efficiently via Neural Logic Inductive Learning

The capability of making interpretable and self-explanatory decisions is...
10/06/2019 ∙ by Yuan Yang, et al. ∙ 0 ∙ share

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Optimal Solution Predictions for Mixed Integer Programs

Mixed Integer Programming (MIP) is one of the most widely used modeling ...
06/23/2019 ∙ by Jian-Ya Ding, et al. ∙ 0 ∙ share

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Compressive Hyperspherical Energy Minimization

Recent work on minimum hyperspherical energy (MHE) has demonstrated its ...
06/12/2019 ∙ by Rongmei Lin, et al. ∙ 0 ∙ share

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Can Graph Neural Networks Help Logic Reasoning?

Effectively combining logic reasoning and probabilistic inference has be...
06/05/2019 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ share

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GLAD: Learning Sparse Graph Recovery

Recovering sparse conditional independence graphs from data is a fundame...
06/01/2019 ∙ by Harsh Shrivastava, et al. ∙ 0 ∙ share

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Exponential Family Estimation via Adversarial Dynamics Embedding

We present an efficient algorithm for maximum likelihood estimation (MLE...
04/27/2019 ∙ by Bo Dai, et al. ∙ 28 ∙ share

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Learning to Plan via Neural Exploration-Exploitation Trees

Sampling-based algorithms such as RRT and its variants are powerful tool...
02/28/2019 ∙ by Binghong Chen, et al. ∙ 0 ∙ share

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Cost-Effective Incentive Allocation via Structured Counterfactual Inference

We address a practical problem ubiquitous in modern industry, in which a...
02/07/2019 ∙ by Romain Lopez, et al. ∙ 8 ∙ share

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Meta Particle Flow for Sequential Bayesian Inference

We present a particle flow realization of Bayes' rule, where an ODE-base...
02/02/2019 ∙ by Xinshi Chen, et al. ∙ 0 ∙ share

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Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

We consider the networked multi-agent reinforcement learning (MARL) prob...
01/27/2019 ∙ by Chao Qu, et al. ∙ 0 ∙ share

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Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

There are great interests as well as many challenges in applying reinfor...
12/27/2018 ∙ by Xinshi Chen, et al. ∙ 0 ∙ share

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Neural Model-Based Reinforcement Learning for Recommendation

There are great interests as well as many challenges in applying reinfor...
12/27/2018 ∙ by Xinshi Chen, et al. ∙ 0 ∙ share

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Double Neural Counterfactual Regret Minimization

Counterfactual Regret Minimization (CRF) is a fundamental and effective ...
12/27/2018 ∙ by Hui Li, et al. ∙ 0 ∙ share

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Bayesian Meta-network Architecture Learning

For deep neural networks, the particular structure often plays a vital r...
12/22/2018 ∙ by Albert Shaw, et al. ∙ 0 ∙ share

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A Policy Gradient Method with Variance Reduction for Uplift Modeling

Uplift modeling aims to directly model the incremental impact of a treat...
11/26/2018 ∙ by Chenchen Li, et al. ∙ 0 ∙ share

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Learning Temporal Point Processes via Reinforcement Learning

Social goods, such as healthcare, smart city, and information networks, ...
11/12/2018 ∙ by Shuang Li, et al. ∙ 0 ∙ share

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Kernel Exponential Family Estimation via Doubly Dual Embedding

We investigate penalized maximum log-likelihood estimation for exponenti...
11/06/2018 ∙ by Bo Dai, et al. ∙ 4 ∙ share

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Latent Dirichlet Allocation for Internet Price War

Internet market makers are always facing intense competitive environment...
08/23/2018 ∙ by Chenchen Li, et al. ∙ 0 ∙ share

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L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

We study instancewise feature importance scoring as a method for model i...
08/08/2018 ∙ by Jianbo Chen, et al. ∙ 0 ∙ share

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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data

Learning nonlinear dynamics from diffusion data is a challenging problem...
07/22/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Adversarial Attack on Graph Structured Data

Deep learning on graph structures has shown exciting results in various ...
06/06/2018 ∙ by Hanjun Dai, et al. ∙ 0 ∙ share

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KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question ...
05/31/2018 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ share

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Learning towards Minimum Hyperspherical Energy

Neural networks are a powerful class of nonlinear functions that can be ...
05/23/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Learning to Optimize via Wasserstein Deep Inverse Optimal Control

We study the inverse optimal control problem in social sciences: we aim ...
05/22/2018 ∙ by Yichen Wang, et al. ∙ 0 ∙ share

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Decoupled Networks

Inner product-based convolution has been a central component of convolut...
04/22/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Iterative Learning with Open-set Noisy Labels

Large-scale datasets possessing clean label annotations are crucial for ...
03/31/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Syntax-Directed Variational Autoencoder for Structured Data

Deep generative models have been enjoying success in modeling continuous...
02/24/2018 ∙ by Hanjun Dai, et al. ∙ 0 ∙ share

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Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

We introduce instancewise feature selection as a methodology for model i...
02/21/2018 ∙ by Jianbo Chen, et al. ∙ 0 ∙ share

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GeniePath: Graph Neural Networks with Adaptive Receptive Paths

We present, GeniePath, a scalable approach for learning adaptive recepti...
02/03/2018 ∙ by Ziqi Liu, et al. ∙ 0 ∙ share

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Smoothed Dual Embedding Control

We revisit the Bellman optimality equation with Nesterov's smoothing tec...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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Boosting the Actor with Dual Critic

This paper proposes a new actor-critic-style algorithm called Dual Actor...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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Deep Hyperspherical Learning

Convolution as inner product has been the founding basis of convolutiona...
11/08/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Towards Black-box Iterative Machine Teaching

In this paper, we make an important step towards the black-box machine t...
10/21/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Le Song

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