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08/15/2023
Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research
X-ray diffraction (XRD) is an essential technique to determine a materia...
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03/19/2023
Unsupervised Learning for Solving the Travelling Salesman Problem
We propose UTSP, an unsupervised learning (UL) framework for solving the...
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09/20/2022
Graph Value Iteration
In recent years, deep Reinforcement Learning (RL) has been successful in...
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07/16/2022
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
Monitoring vegetation productivity at extremely fine resolutions is valu...
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12/02/2021
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Multi-label classification (MLC) is a prediction task where each sample ...
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11/17/2021
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
Climate change is posing new challenges to crop-related concerns includi...
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10/21/2021
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Reinforcement learning (RL) experiments have notoriously high variance, ...
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10/03/2021
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
In recent years, we have witnessed tremendous progress in deep reinforce...
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08/21/2021
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
Crystal-structure phase mapping is a core, long-standing challenge in ma...
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06/08/2021
The Fast Kernel Transform
Kernel methods are a highly effective and widely used collection of mode...
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06/04/2021
Materials Representation and Transfer Learning for Multi-Property Prediction
The adoption of machine learning in materials science has rapidly transf...
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06/02/2021
Towards Deeper Deep Reinforcement Learning
In computer vision and natural language processing, innovations in model...
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02/26/2021
Low-Precision Reinforcement Learning
Low-precision training has become a popular approach to reduce computati...
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02/05/2021
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
There has been an increasing interest in harnessing deep learning to tac...
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01/19/2021
Autonomous synthesis of metastable materials
Autonomous experimentation enabled by artificial intelligence (AI) offer...
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10/30/2020
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
A key problem in computational sustainability is to understand the distr...
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06/04/2020
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
Despite significant progress in general AI planning, certain domains rem...
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06/03/2019
Deep Reasoning Networks: Thinking Fast and Slow
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework t...
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02/25/2019
Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant
In this work, we consider applying machine learning to the analysis and ...
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11/01/2018
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Citizen science projects are successful at gathering rich datasets for v...
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03/22/2018
End-to-End Learning for the Deep Multivariate Probit Model
The multivariate probit model (MVP) is a popular classic model for study...
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09/17/2017
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlation...
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05/23/2017
XOR-Sampling for Network Design with Correlated Stochastic Events
Many network optimization problems can be formulated as stochastic netwo...
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10/08/2016
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Arising from many applications at the intersection of decision making an...
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10/03/2016
Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery
High-Throughput materials discovery involves the rapid synthesis, measur...
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08/17/2015
Variable Elimination in the Fourier Domain
The ability to represent complex high dimensional probability distributi...
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09/26/2013
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Many probabilistic inference tasks involve summations over exponentially...
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02/27/2013
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
Integration is affected by the curse of dimensionality and quickly becom...
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02/06/2013
Algorithm Portfolio Design: Theory vs. Practice
Stochastic algorithms are among the best for solving computationally har...
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01/10/2013
A Bayesian Approach to Tackling Hard Computational Problems
We are developing a general framework for using learned Bayesian models ...
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10/16/2012
Uniform Solution Sampling Using a Constraint Solver As an Oracle
We consider the problem of sampling from solutions defined by a set of h...
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03/15/2012