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06/15/2023
Document Entity Retrieval with Massive and Noisy Pre-training
Visually-Rich Document Entity Retrieval (VDER) is a type of machine lear...
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06/03/2023
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas
Search is an important technique in program synthesis that allows for ad...
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05/26/2023
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Combinatorial optimization (CO) problems are often NP-hard and thus out ...
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03/07/2023
Gradient-Free Structured Pruning with Unlabeled Data
Large Language Models (LLMs) have achieved great success in solving diff...
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01/31/2023
Learning Universal Policies via Text-Guided Video Generation
A goal of artificial intelligence is to construct an agent that can solv...
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11/30/2022
Score-based Continuous-time Discrete Diffusion Models
Score-based modeling through stochastic differential equations (SDEs) ha...
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11/14/2022
Learning to Optimize with Stochastic Dominance Constraints
In real-world decision-making, uncertainty is important yet difficult to...
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09/16/2022
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Optimal scaling has been well studied for Metropolis-Hastings (M-H) algo...
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07/23/2022
Annealed Training for Combinatorial Optimization on Graphs
The hardness of combinatorial optimization (CO) problems hinders collect...
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06/29/2022
Discrete Langevin Sampler via Wasserstein Gradient Flow
Recently, a family of locally balanced (LB) samplers has demonstrated ex...
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03/20/2022
CrossBeam: Learning to Search in Bottom-Up Program Synthesis
Many approaches to program synthesis perform a search within an enormous...
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12/01/2021
Neural Stochastic Dual Dynamic Programming
Stochastic dual dynamic programming (SDDP) is a state-of-the-art method ...
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10/28/2021
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
Knowledge graphs (KGs) capture knowledge in the form of head–relation–ta...
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07/12/2021
Combiner: Full Attention Transformer with Sparse Computation Cost
Transformers provide a class of expressive architectures that are extrem...
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06/26/2021
SpreadsheetCoder: Formula Prediction from Semi-structured Context
Spreadsheet formula prediction has been an important program synthesis p...
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11/10/2020
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Discrete structures play an important role in applications like program ...
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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...
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06/29/2020
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Retrosynthetic planning is a critical task in organic chemistry which id...
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06/28/2020
Scalable Deep Generative Modeling for Sparse Graphs
Learning graph generative models is a challenging task for deep learning...
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06/09/2020
Learning to Stop While Learning to Predict
There is a recent surge of interest in designing deep architectures base...
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03/17/2020
Energy-Based Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangea...
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02/16/2020
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i.e., finding the k largest or smallest elements fr...
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01/06/2020
Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
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10/28/2019
Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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06/01/2019
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), wh...
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04/27/2019
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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02/02/2019
Meta Particle Flow for Sequential Bayesian Inference
We present a particle flow realization of Bayes' rule, where an ODE-base...
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12/04/2018
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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11/06/2018
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponenti...
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06/06/2018
Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various ...
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05/31/2018
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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02/24/2018
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
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09/12/2017
Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question ans...
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05/16/2017
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The availability of large scale event data with time stamps has given ri...
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04/05/2017
Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NP-hard co...
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09/01/2015
Online Supervised Subspace Tracking
We present a framework for supervised subspace tracking, when there are ...
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07/05/2015
Scan B-Statistic for Kernel Change-Point Detection
Detecting the emergence of an abrupt change-point is a classic problem i...
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06/09/2015
Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
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04/23/2014