
Solving Mixed Integer Programs Using Neural Networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...
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Strong Generalization and Efficiency in Neural Programs
We study the problem of learning efficient algorithms that strongly gene...
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Scalable Deep Generative Modeling for Sparse Graphs
Learning graph generative models is a challenging task for deep learning...
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A sparse negative binomial mixture model for clustering RNAseq count data
Clustering with variable selection is a challenging but critical task fo...
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Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving
We propose prioritized unit propagation with periodic resetting, which i...
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Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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Simultaneous Estimation of Number of Clusters and Feature Sparsity in Clustering HighDimensional Data
Estimating the number of clusters (K) is a critical and often difficult ...
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Fast Training of Sparse Graph Neural Networks on Dense Hardware
Graph neural networks have become increasingly popular in recent years d...
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Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records
Effective modeling of electronic health records (EHR) is rapidly becomin...
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REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
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Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This paper addresses the challenging problem of retrieval and matching o...
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Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
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Proximal Policy Optimization and its Dynamic Version for Sequence Generation
In sequence generation task, many works use policy gradient for model op...
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Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that impro...
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Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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Learning Deep Generative Models of Graphs
Graphs are fundamental data structures which concisely capture the relat...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Learning modelbased planning from scratch
Conventional wisdom holds that modelbased planning is a powerful approa...
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Dualing GANs
Generative adversarial nets (GANs) are a promising technique for modelin...
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Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
We study characteristics of receptive fields of units in deep convolutio...
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Gated Graph Sequence Neural Networks
Graphstructured data appears frequently in domains including chemistry,...
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The Variational Fair Autoencoder
We investigate the problem of learning representations that are invarian...
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Generative Moment Matching Networks
We consider the problem of learning deep generative models from data. We...
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Learning unbiased features
A key element in transfer learning is representation learning; if repres...
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MeanField Networks
The mean field algorithm is a widely used approximate inference algorith...
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Yujia Li
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