no code implementations • ICML 2020 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John Gregoire, Carla Gomes
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with constraint reasoning for solving pattern de-mixing problems, typically in an unsupervised or very-weakly-supervised setting.
no code implementations • NeurIPS 2023 • Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods.
1 code implementation • 27 Oct 2023 • Sebastian Ament, Andrew Witte, Nishant Garg, Julius Kusuma
Herein, we 1) propose modeling steps that make concrete strength amenable to be predicted accurately by a Gaussian process model with relatively few measurements, 2) formulate the search for sustainable concrete as a multi-objective optimization problem, and 3) leverage the proposed model to carry out multi-objective BO with real-world strength measurements of the algorithmically proposed mixes.
1 code implementation • 15 Aug 2023 • Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson
X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes.
1 code implementation • 3 Mar 2023 • Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
We use Bayesian Optimization (BO) and propose a novel surrogate modeling approach for efficiently handling a large number of binary and categorical parameters.
1 code implementation • 16 Jun 2022 • Sebastian Ament, Carla Gomes
To improve the performance of BO, prior work suggested incorporating gradient information into a Gaussian process surrogate of the objective, giving rise to kernel matrices of size $nd \times nd$ for $n$ observations in $d$ dimensions.
no code implementations • 2 Dec 2021 • Guillaume Perez, Sebastian Ament, Carla Gomes, Arnaud Lallouet
Machine learning models are widely used for real-world applications, such as document analysis and vision.
no code implementations • 21 Aug 2021 • Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
1 code implementation • 11 Jun 2021 • Sebastian Ament, Carla Gomes
Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression.
1 code implementation • 8 Jun 2021 • John Paul Ryan, Sebastian Ament, Carla P. Gomes, Anil Damle
Kernel methods are a highly effective and widely used collection of modern machine learning algorithms.
no code implementations • 19 Jan 2021 • Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, R. Bruce van Dover
Autonomous experimentation enabled by artificial intelligence (AI) offers a new paradigm for accelerating scientific discovery.
no code implementations • 7 Sep 2020 • Guillaume Perez, Sebastian Ament, Carla Gomes, Michel Barlaud
In this paper we propose three new efficient algorithms for projecting any vector of finite length onto the weighted $\ell_1$ ball.
no code implementations • 25 Sep 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.
no code implementations • 3 Jun 2019 • Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module.
no code implementations • 14 Feb 2019 • Sebastian Ament, John Gregoire, Carla Gomes
In particular, we demonstrate the effectiveness of PMF in conjunction with the EMG mixture model on synthetic data and two real-world applications: X-ray diffraction and Raman spectroscopy.