b'Yujia Li'

research

∙ 07/31/2023

Detecting the Anomalies in LiDAR Pointcloud

LiDAR sensors play an important role in the perception stack of modern a...

0 Chiyu Zhang, et al. ∙

research

∙ 03/25/2023

Causal Image Synthesis of Brain MR in 3D

Clinical decision making requires counterfactual reasoning based on a fa...

0 Yujia Li, et al. ∙

research

∙ 01/31/2023

Transformers Meet Directed Graphs

Transformers were originally proposed as a sequence-to-sequence model fo...

0 Simon Geisler, et al. ∙

research

∙ 02/08/2022

Competition-Level Code Generation with AlphaCode

Programming is a powerful and ubiquitous problem-solving tool. Developin...

0 Yujia Li, et al. ∙

research

∙ 12/10/2021

Association study between gene expression and multiple phenotypes in omics applications of complex diseases

Studying phenotype-gene association can uncover mechanism of diseases an...

0 Yujia Li, et al. ∙

research

∙ 08/25/2021

ETA Prediction with Graph Neural Networks in Google Maps

Travel-time prediction constitutes a task of high importance in transpor...

13 Austin Derrow-Pinion, et al. ∙

research

∙ 07/20/2021

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

We present a new dataset of Wikipedia articles each paired with a knowle...

65 Luyu Wang, et al. ∙

research

∙ 05/06/2021

Computer-Aided Design as Language

Computer-Aided Design (CAD) applications are used in manufacturing to mo...

64 Yaroslav Ganin, et al. ∙

research

∙ 12/23/2020

Solving Mixed Integer Programs Using Neural Networks

Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...

9 Vinod Nair, et al. ∙

research

∙ 07/07/2020

Strong Generalization and Efficiency in Neural Programs

We study the problem of learning efficient algorithms that strongly gene...

11 Yujia Li, et al. ∙

research

∙ 06/28/2020

Scalable Deep Generative Modeling for Sparse Graphs

Learning graph generative models is a challenging task for deep learning...

8 Hanjun Dai, et al. ∙

research

∙ 12/05/2019

A sparse negative binomial mixture model for clustering RNA-seq count data

Clustering with variable selection is a challenging but critical task fo...

8 Tanbin Rahman, et al. ∙

research

∙ 12/04/2019

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...

0 Xujie Si, et al. ∙

research

∙ 10/28/2019

Learning Transferable Graph Exploration

This paper considers the problem of efficient exploration of unseen envi...

12 Hanjun Dai, et al. ∙

research

∙ 10/02/2019

Efficient Graph Generation with Graph Recurrent Attention Networks

We propose a new family of efficient and expressive deep generative mode...

16 Renjie Liao, et al. ∙

research

∙ 09/04/2019

Simultaneous Estimation of Number of Clusters and Feature Sparsity in Clustering High-Dimensional Data

Estimating the number of clusters (K) is a critical and often difficult ...

0 Yujia Li, et al. ∙

research

∙ 06/27/2019

Fast Training of Sparse Graph Neural Networks on Dense Hardware

Graph neural networks have become increasingly popular in recent years d...

4 Matej Balog, et al. ∙

research

∙ 06/11/2019

Graph Convolutional Transformer: Learning the Graphical Structure of Electronic Health Records

Effective modeling of electronic health records (EHR) is rapidly becomin...

6 Edward Choi, et al. ∙

research

∙ 05/07/2019

REGAL: Transfer Learning For Fast Optimization of Computation Graphs

We present a deep reinforcement learning approach to optimizing the exec...

14 Aditya Paliwal, et al. ∙

research

∙ 04/29/2019

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

This paper addresses the challenging problem of retrieval and matching o...

8 Yujia Li, et al. ∙

research

∙ 12/04/2018

Compositional Imitation Learning: Explaining and executing one task at a time

We introduce a framework for Compositional Imitation Learning and Execut...

4 Thomas Kipf, et al. ∙

research

∙ 08/24/2018

Proximal Policy Optimization and its Dynamic Version for Sequence Generation

In sequence generation task, many works use policy gradient for model op...

0 Yi-Lin Tuan, et al. ∙

research

∙ 06/05/2018

Relational Deep Reinforcement Learning

We introduce an approach for deep reinforcement learning (RL) that impro...

0 Vinicius Zambaldi, et al. ∙

research

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

0 Peter W. Battaglia, et al. ∙

research

∙ 03/08/2018

Learning Deep Generative Models of Graphs

Graphs are fundamental data structures which concisely capture the relat...

0 Yujia Li, et al. ∙

research

∙ 07/19/2017

Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture f...

0 Theophane Weber, et al. ∙

research

∙ 07/19/2017

Learning model-based planning from scratch

Conventional wisdom holds that model-based planning is a powerful approa...

0 Razvan Pascanu, et al. ∙

research

∙ 06/19/2017

Dualing GANs

Generative adversarial nets (GANs) are a promising technique for modelin...

0 Yujia Li, et al. ∙

research

∙ 01/15/2017

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

We study characteristics of receptive fields of units in deep convolutio...

0 Wenjie Luo, et al. ∙

research

∙ 11/17/2015

Gated Graph Sequence Neural Networks

Graph-structured data appears frequently in domains including chemistry,...

0 Yujia Li, et al. ∙

research

∙ 11/03/2015

The Variational Fair Autoencoder

We investigate the problem of learning representations that are invarian...

0 Christos Louizos, et al. ∙

research

∙ 02/10/2015

Generative Moment Matching Networks

We consider the problem of learning deep generative models from data. We...

0 Yujia Li, et al. ∙

research

∙ 12/17/2014

Learning unbiased features

A key element in transfer learning is representation learning; if repres...

0 Yujia Li, et al. ∙

research

∙ 10/21/2014

Mean-Field Networks

The mean field algorithm is a widely used approximate inference algorith...

0 Yujia Li, et al. ∙

Yujia Li

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