15 citations · 77 across the 18 of their papers we have counts for
19 papers
Unsupervised Learning for Solving the Travelling Salesman Problem
Yimeng Min, Yiwei Bai, Carla P. Gomes
We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss. The GNN ou…
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction
Junwen Bai, Yuanqi Du, Yingheng Wang +3
Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property…
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net
Joshua Fan, Di Chen, Jiaming Wen +2
Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of…
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
Sebastian Ament, Carla Gomes
Bayesian Optimization (BO) has shown great promise for the global optimization of functions that are expensive to evaluate, but despite many successes, standard approaches can stru…
Constrained Machine Learning: The Bagel Framework
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Machine learning models are widely used for real-world applications, such as document analysis and vision. Constrained machine learning problems are problems where learned models h…
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai, Shufeng Kong, Carla P. Gomes
Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model b…
A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction
Joshua Fan, Junwen Bai, Zhiyun Li +2
Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield pre…
Contrastively Disentangled Sequential Variational Autoencoder
Junwen Bai, Weiran Wang, Carla Gomes
Self-supervised disentangled representation learning is a critical task in sequence modeling. The learnt representations contribute to better model interpretability as well as the…
Is High Variance Unavoidable in RL? A Case Study in Continuous Control
Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
Reinforcement learning (RL) experiments have notoriously high variance, and minor details can have disproportionately large effects on measured outcomes. This is problematic for cr…
Sparse Bayesian Learning via Stepwise Regression
Sebastian Ament, Carla Gomes
Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Mat…
On the Optimality of Backward Regression: Sparse Recovery and Subset Selection
Sebatian Ament, Carla Gomes
Sparse recovery and subset selection are fundamental problems in varied communities, including signal processing, statistics and machine learning. Herein, we focus on an important…
Towards Deeper Deep Reinforcement Learning with Spectral Normalization
Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger
In computer vision and natural language processing, innovations in model architecture that increase model capacity have reliably translated into gains in performance. In stark cont…
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Wenting Zhao, Shufeng Kong, Junwen Bai +2
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global chang…
Evaluating Multi-label Classifiers with Noisy Labels
Wenting Zhao, Carla Gomes
Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to dea…
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
Johan Bjorck, Xiangyu Chen, Christopher De Sa +2
Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising ap…
Understanding Decoupled and Early Weight Decay
Johan Bjorck, Kilian Weinberger, Carla Gomes
Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recen…
Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation
Shufeng Kong, Junwen Bai, Jae Hee Lee +6
A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale predicti…
Efficient Projection Algorithms onto the Weighted l1 Ball
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Projected gradient descent has been proved efficient in many optimization and machine learning problems. The weighted ball has been shown effective in sparse system identi…
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…