153 citations · 261 across the 10 of their papers we have counts for
10 papers
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
Surrogate-based Autotuning for Randomized Sketching Algorithms in Regression Problems
Younghyun Cho, James W. Demmel, Michał Dereziński +4
Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical perfor…
CLOVER : Probabilistic Forecasting with Coherent Learning Objective Reparameterization
Kin G. Olivares, Geoffrey Négiar, Ruijun Ma +3
Obtaining accurate probabilistic forecasts is an operational challenge in many applications, such as energy management, climate forecasting, supply chain planning, and resource all…
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Pu Ren, N. Benjamin Erichson, Junyi Guo +4
Super-resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.…
Lipschitz Recurrent Neural Networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga +2
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-unders…
Large batch size training of neural networks with adversarial training and second-order information
Zhewei Yao, Amir Gholami, Daiyaan Arfeen +4
The most straightforward method to accelerate Stochastic Gradient Descent (SGD) computation is to distribute the randomly selected batch of inputs over multiple processors. To keep…