b'Le Song'

research

∙ 06/28/2022

Graph Condensation via Receptive Field Distribution Matching

Graph neural networks (GNNs) enable the analysis of graphs using deep le...

Mengyang Liu, et al. ∙

research

∙ 05/27/2022

Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition

Weakly supervised named entity recognition methods train label models to...

Yinghao Li, et al. ∙

research

∙ 03/18/2022

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

Weakly-supervised learning (WSL) has shown promising results in addressi...

Rongzhi Zhang, et al. ∙

research

∙ 02/11/2022

Learning Temporal Rules from Noisy Timeseries Data

Events across a timeline are a common data representation, seen in diffe...

Karan Samel, et al. ∙

research

∙ 12/14/2021

Efficient Dynamic Graph Representation Learning at Scale

Dynamic graphs with ordered sequences of events between nodes are preval...

Xinshi Chen, et al. ∙

research

∙ 11/22/2021

A General Framework for Lifelong Localization and Mapping in Changing Environment

The environment of most real-world scenarios such as malls and supermark...

Min Zhao, et al. ∙

research

∙ 11/03/2021

Multi-task Learning of Order-Consistent Causal Graphs

We consider the problem of discovering K related Gaussian directed acycl...

Xinshi Chen, et al. ∙

research

∙ 10/27/2021

RoMA: Robust Model Adaptation for Offline Model-based Optimization

We consider the problem of searching an input maximizing a black-box obj...

Sihyun Yu, et al. ∙

research

∙ 10/26/2021

Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approa...

Yu-Ying Liu, et al. ∙

research

∙ 10/07/2021

GNN is a Counter? Revisiting GNN for Question Answering

Question Answering (QA) has been a long-standing research topic in AI an...

Kuan Wang, et al. ∙

research

∙ 10/02/2021

ProTo: Program-Guided Transformer for Program-Guided Tasks

Programs, consisting of semantic and structural information, play an imp...

Zelin Zhao, et al. ∙

research

∙ 05/26/2021

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

We study the problem of learning a named entity recognition (NER) tagger...

Yinghao Li, et al. ∙

research

∙ 05/17/2021

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Dynamic graph modeling has recently attracted much attention due to its ...

Lu Wang, et al. ∙

research

∙ 04/25/2021

Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

A wide range of modern science and engineering applications are formulat...

Yuyu Zhang, et al. ∙

research

∙ 03/22/2021

How to Design Sample and Computationally Efficient VQA Models

In multi-modal reasoning tasks, such as visual question answering (VQA),...

Karan Samel, et al. ∙

research

∙ 03/18/2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented...

James Fox, et al. ∙

research

∙ 03/01/2021

A Biased Graph Neural Network Sampler with Near-Optimal Regret

Graph neural networks (GNN) have recently emerged as a vehicle for apply...

Qingru Zhang, et al. ∙

research

∙ 11/04/2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

The design/discovery of new materials is highly non-trivial owing to the...

Rohit Batra, et al. ∙

research

∙ 09/16/2020

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

Open-domain multi-hop question answering (QA) requires to retrieve multi...

Yuyu Zhang, et al. ∙

research

∙ 09/16/2020

Question Directed Graph Attention Network for Numerical Reasoning over Text

Numerical reasoning over texts, such as addition, subtraction, sorting a...

Kunlong Chen, et al. ∙

research

∙ 06/29/2020

Retro: Learning Retrosynthetic Planning with Neural Guided A Search

Retrosynthetic planning is a critical task in organic chemistry which id...

Binghong Chen, et al. ∙

research

∙ 06/24/2020

Understanding Deep Architectures with Reasoning Layer

Recently, there has been a surge of interest in combining deep learning ...

Xinshi Chen, et al. ∙

research

∙ 06/10/2020

Bandit Samplers for Training Graph Neural Networks

Several sampling algorithms with variance reduction have been proposed f...

Ziqi Liu, et al. ∙

research

∙ 06/09/2020

Learning to Stop While Learning to Predict

There is a recent surge of interest in designing deep architectures base...

Xinshi Chen, et al. ∙

research

∙ 04/19/2020

Intention Propagation for Multi-agent Reinforcement Learning

A hallmark of an AI agent is to mimic human beings to understand and int...

Chao Qu, et al. ∙

research

∙ 04/09/2020

Orthogonal Over-Parameterized Training

The inductive bias of a neural network is largely determined by the arch...

Weiyang Liu, et al. ∙

research

∙ 02/28/2020

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

Recent studies on open-domain question answering have achieved prominent...

Yuyu Zhang, et al. ∙

research

∙ 02/27/2020

Heterogeneous Graph Neural Networks for Malicious Account Detection

We present, GEM, the first heterogeneous graph neural network approach f...

Ziqi Liu, et al. ∙

research

∙ 02/13/2020

RNA Secondary Structure Prediction By Learning Unrolled Algorithms

In this paper, we propose an end-to-end deep learning model, called E2Ef...

Xinshi Chen, et al. ∙

research

∙ 01/29/2020

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Markov Logic Networks (MLNs), which elegantly combine logic rules and pr...

Yuyu Zhang, et al. ∙

research

∙ 01/06/2020

Retrosynthesis Prediction with Conditional Graph Logic Network

Retrosynthesis is one of the fundamental problems in organic chemistry. ...

Hanjun Dai, et al. ∙

research

∙ 11/13/2019

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...

Xuesong Shi, et al. ∙

research

∙ 10/28/2019

Neural Similarity Learning

Inner product-based convolution has been the founding stone of convoluti...

Weiyang Liu, et al. ∙

research

∙ 10/06/2019

Learn to Explain Efficiently via Neural Logic Inductive Learning

The capability of making interpretable and self-explanatory decisions is...

Yuan Yang, et al. ∙

research

∙ 06/23/2019

Optimal Solution Predictions for Mixed Integer Programs

Mixed Integer Programming (MIP) is one of the most widely used modeling ...

Jian-Ya Ding, et al. ∙

research

∙ 06/12/2019

Compressive Hyperspherical Energy Minimization

Recent work on minimum hyperspherical energy (MHE) has demonstrated its ...

Rongmei Lin, et al. ∙

research

∙ 06/05/2019

Can Graph Neural Networks Help Logic Reasoning?

Effectively combining logic reasoning and probabilistic inference has be...

Yuyu Zhang, et al. ∙

research

∙ 06/01/2019

GLAD: Learning Sparse Graph Recovery

Recovering sparse conditional independence graphs from data is a fundame...

Harsh Shrivastava, et al. ∙

research

∙ 04/27/2019

Exponential Family Estimation via Adversarial Dynamics Embedding

We present an efficient algorithm for maximum likelihood estimation (MLE...

Bo Dai, et al. ∙

research

∙ 02/28/2019

Learning to Plan via Neural Exploration-Exploitation Trees

Sampling-based algorithms such as RRT and its variants are powerful tool...

Binghong Chen, et al. ∙

research

∙ 02/07/2019

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

We address a practical problem ubiquitous in modern industry, in which a...

Romain Lopez, et al. ∙

research

∙ 02/02/2019

Meta Particle Flow for Sequential Bayesian Inference

We present a particle flow realization of Bayes' rule, where an ODE-base...

Xinshi Chen, et al. ∙

research

∙ 01/27/2019

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

We consider the networked multi-agent reinforcement learning (MARL) prob...

Chao Qu, et al. ∙

research

∙ 12/27/2018

Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

There are great interests as well as many challenges in applying reinfor...

Xinshi Chen, et al. ∙

research

∙ 12/27/2018

Neural Model-Based Reinforcement Learning for Recommendation

There are great interests as well as many challenges in applying reinfor...

Xinshi Chen, et al. ∙

research

∙ 12/27/2018

Double Neural Counterfactual Regret Minimization

Counterfactual Regret Minimization (CRF) is a fundamental and effective ...

Hui Li, et al. ∙

research

∙ 12/22/2018

Bayesian Meta-network Architecture Learning

For deep neural networks, the particular structure often plays a vital r...

Albert Shaw, et al. ∙

research

∙ 11/26/2018

A Policy Gradient Method with Variance Reduction for Uplift Modeling

Uplift modeling aims to directly model the incremental impact of a treat...

Chenchen Li, et al. ∙

research

∙ 11/12/2018

Learning Temporal Point Processes via Reinforcement Learning

Social goods, such as healthcare, smart city, and information networks, ...

Shuang Li, et al. ∙

research

∙ 11/06/2018

Kernel Exponential Family Estimation via Doubly Dual Embedding

We investigate penalized maximum log-likelihood estimation for exponenti...

Bo Dai, et al. ∙

Le Song

Featured Co-authors