16 citations · 45 across the 8 of their papers we have counts for
8 papers · 1 filter
PairIT: Autoregressive Transformers for Low-Data Molecule Optimization
Hannah Lawrence, Bodhi P. Vani, Ji Won Park +5
Generating novel molecules with desired properties is a central challenge in drug discovery. Although foundational work focused on de novo generation, practical use cases require a…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Grégoire Mialon, Quentin Garrido, Hannah Lawrence +3
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering…
GULP: a prediction-based metric between representations
Enric Boix-Adsera, Hannah Lawrence, George Stepaniants +1
Comparing the representations learned by different neural networks has recently emerged as a key tool to understand various architectures and ultimately optimize them. In this work…
Distilling Model Failures as Directions in Latent Space
Saachi Jain, Hannah Lawrence, Ankur Moitra +1
Existing methods for isolating hard subpopulations and spurious correlations in datasets often require human intervention. This can make these methods labor-intensive and dataset-s…
Implicit Bias of Linear Equivariant Networks
Hannah Lawrence, Kristian Georgiev, Andrew Dienes +1
Group equivariant convolutional neural networks (G-CNNs) are generalizations of convolutional neural networks (CNNs) which excel in a wide range of technical applications by explic…
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Hannah Lawrence, David A. Barmherzig, Henry Li +2
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imag…
Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition
Lin Chen, Qian Yu, Hannah Lawrence +1
We study the problem of switching-constrained online convex optimization (OCO), where the player has a limited number of opportunities to change her action. While the discrete anal…