Random Field Augmentations for Self-Supervised Representation Learning

no code implementations • 7 Nov 2023 • Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar, Karan Singhal

In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning.

Representation Learning

Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout

no code implementations • 11 Sep 2023 • Pengfei Guo, Warren Richard Morningstar, Raviteja Vemulapalli, Karan Singhal, Vishal M. Patel, Philip Andrew Mansfield

To mitigate this issue and facilitate training of large models on edge devices, we introduce a simple yet effective strategy, Federated Layer-wise Learning, to simultaneously reduce per-client memory, computation, and communication costs.

Federated Learning Representation Learning +1

Federated Variational Inference: Towards Improved Personalization and Generalization

no code implementations • 23 May 2023 • Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar

We then approximate this process using Variational Inference to train our model efficiently.

Bayesian Inference Federated Learning +3

Federated Training of Dual Encoding Models on Small Non-IID Client Datasets

no code implementations • 30 Sep 2022 • Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner, Karan Singhal, Arash Afkanpour, Bradley Green

In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.

Federated Learning Representation Learning