no code implementations • 24 Oct 2020 • Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar, Jian Peng, Gilbert Holder
Dark matter substructures are interesting since they can reveal the properties of dark matter.
Cosmology and Nongalactic Astrophysics Computational Physics
1 code implementation • ICLR 2022 • Honglin Yuan, Warren Morningstar, Lin Ning, Karan Singhal
Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap).
no code implementations • 18 Nov 2022 • Yangjun Ruan, Saurabh Singh, Warren Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning.
no code implementations • 2 Dec 2023 • Neha Kalibhat, Warren Morningstar, Alex Bijamov, Luyang Liu, Karan Singhal, Philip Mansfield
We define augmentations in frequency space called Fourier Domain Augmentations (FDA) and show that training SSL models on a combination of these and image augmentations can improve the downstream classification accuracy by up to 1. 3% on ImageNet-1K.
no code implementations • 8 Mar 2024 • Warren Morningstar, Alex Bijamov, Chris Duvarney, Luke Friedman, Neha Kalibhat, Luyang Liu, Philip Mansfield, Renan Rojas-Gomez, Karan Singhal, Bradley Green, Sushant Prakash
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL).