
BERTifying the Hidden Markov Model for MultiSource Weakly Supervised Named Entity Recognition
We study the problem of learning a named entity recognition (NER) tagger...
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TCL: Transformerbased Dynamic Graph Modelling via Contrastive Learning
Dynamic graph modeling has recently attracted much attention due to its ...
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Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients
A wide range of modern science and engineering applications are formulat...
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How to Design Sample and Computationally Efficient VQA Models
In multimodal reasoning tasks, such as visual question answering (VQA),...
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Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented...
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A Biased Graph Neural Network Sampler with NearOptimal Regret
Graph neural networks (GNN) have recently emerged as a vehicle for apply...
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Polymers for Extreme Conditions Designed Using SyntaxDirected Variational Autoencoders
The design/discovery of new materials is highly nontrivial owing to the...
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DDRQA: Dynamic Document Reranking for Opendomain Multihop Question Answering
Opendomain multihop question answering (QA) requires to retrieve multi...
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Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting a...
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Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Retrosynthetic planning is a critical task in organic chemistry which id...
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Understanding Deep Architectures with Reasoning Layer
Recently, there has been a surge of interest in combining deep learning ...
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Bandit Samplers for Training Graph Neural Networks
Several sampling algorithms with variance reduction have been proposed f...
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Learning to Stop While Learning to Predict
There is a recent surge of interest in designing deep architectures base...
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Intention Propagation for Multiagent Reinforcement Learning
A hallmark of an AI agent is to mimic human beings to understand and int...
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Orthogonal OverParameterized Training
The inductive bias of a neural network is largely determined by the arch...
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DCBERT: Decoupling Question and Document for Efficient Contextual Encoding
Recent studies on opendomain question answering have achieved prominent...
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Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach f...
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RNA Secondary Structure Prediction By Learning Unrolled Algorithms
In this paper, we propose an endtoend deep learning model, called E2Ef...
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Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Markov Logic Networks (MLNs), which elegantly combine logic rules and pr...
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Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
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Are We Ready for Service Robots? The OpenLORISScene Datasets for Lifelong SLAM
Service robots should be able to operate autonomously in dynamic and dai...
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Neural Similarity Learning
Inner productbased convolution has been the founding stone of convoluti...
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Learn to Explain Efficiently via Neural Logic Inductive Learning
The capability of making interpretable and selfexplanatory decisions is...
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Optimal Solution Predictions for Mixed Integer Programs
Mixed Integer Programming (MIP) is one of the most widely used modeling ...
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Compressive Hyperspherical Energy Minimization
Recent work on minimum hyperspherical energy (MHE) has demonstrated its ...
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Can Graph Neural Networks Help Logic Reasoning?
Effectively combining logic reasoning and probabilistic inference has be...
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GLAD: Learning Sparse Graph Recovery
Recovering sparse conditional independence graphs from data is a fundame...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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Learning to Plan via Neural ExplorationExploitation Trees
Samplingbased algorithms such as RRT and its variants are powerful tool...
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CostEffective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern industry, in which a...
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Meta Particle Flow for Sequential Bayesian Inference
We present a particle flow realization of Bayes' rule, where an ODEbase...
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Value Propagation for Decentralized Networked Deep Multiagent Reinforcement Learning
We consider the networked multiagent reinforcement learning (MARL) prob...
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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...
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Neural ModelBased Reinforcement Learning for Recommendation
There are great interests as well as many challenges in applying reinfor...
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Double Neural Counterfactual Regret Minimization
Counterfactual Regret Minimization (CRF) is a fundamental and effective ...
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Bayesian Metanetwork Architecture Learning
For deep neural networks, the particular structure often plays a vital r...
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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...
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Learning Temporal Point Processes via Reinforcement Learning
Social goods, such as healthcare, smart city, and information networks, ...
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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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Latent Dirichlet Allocation for Internet Price War
Internet market makers are always facing intense competitive environment...
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LShapley and CShapley: Efficient Model Interpretation for Structured Data
We study instancewise feature importance scoring as a method for model i...
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Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
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Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various ...
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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 ...
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Learning towards Minimum Hyperspherical Energy
Neural networks are a powerful class of nonlinear functions that can be ...
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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 ...
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Decoupled Networks
Inner productbased convolution has been a central component of convolut...
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Iterative Learning with Openset Noisy Labels
Largescale datasets possessing clean label annotations are crucial for ...
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SyntaxDirected Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
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Learning to Explain: An InformationTheoretic Perspective on Model Interpretation
We introduce instancewise feature selection as a methodology for model i...
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Le Song
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Associate Director, Center for Machine Learning, Associate Professor at Georgia Institute of Technology