Search Results for author: Warren Morningstar

Found 5 papers, 1 papers with code

Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks

no code implementations24 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

What Do We Mean by Generalization in Federated Learning?

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).

Federated Learning

Weighted Ensemble Self-Supervised Learning

no code implementations18 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.

Self-Supervised Learning

Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations

no code implementations2 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.

Data Augmentation Self-Supervised Learning +1

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