107 citations · 287 across the 17 of their papers we have counts for
5 papers · 1 filter
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato, Simon Guiroy, Ethan Caballero +2
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs ac…
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen, Irina Rish
A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. Automating the process of choosing the best forecasting model and i…
SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
Soroosh Shahtalebi, Jean-Christophe Gagnon-Audet, Touraj Laleh +3
A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data distribution is not i.i.d to the training…
Gradient Masked Federated Optimization
Irene Tenison, Sreya Francis, Irina Rish
Federated Averaging (FedAVG) has become the most popular federated learning algorithm due to its simplicity and low communication overhead. We use simple examples to show that FedA…
Towards Causal Federated Learning For Enhanced Robustness and Privacy
Sreya Francis, Irene Tenison, Irina Rish
Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating de…