3.6k citations · 10.6k across the 54 of their papers we have counts for
12 papers · 1 filter
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas +5
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a…
Dataset of Random Relaxations for Crystal Structure Search of Li-Si System
Gowoon Cheon, Lusann Yang, Kevin McCloskey +2
Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials fro…
Self-assembling kinetics: Accessing a new design space via differentiable statistical-physics models
Carl P. Goodrich, Ella M. King, Samuel S. Schoenholz +2
The inverse problem of designing component interactions to target emergent structure is fundamental to numerous applications in biotechnology, materials science, and statistical ph…
Does Data Augmentation Benefit from Split BatchNorms
Amil Merchant, Barret Zoph, Ekin Dogus Cubuk
Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision. However, state-o…
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson +4
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectur…
Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica
Ekin D. Cubuk, Andrea J. Liu, Efthimios Kaxiras +1
The fragility of a glassforming liquid characterizes how rapidly its relaxation dynamics slow down with cooling. The viscosity of strong liquids follows an Arrhenius law with a tem…