99 citations · 219 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
Di Chen, Yiwei Bai, Sebastian Ament +7
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials.…
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…
The Fast Kernel Transform
John Paul Ryan, Sebastian Ament, Carla P. Gomes +1
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms. A fundamental limitation of virtually all such methods are computations invo…
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…
Autonomous synthesis of metastable materials
Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland +7
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery. Non-equilibrium materials synthesis is emblematic of…