29 citations · 144 across the 30 of their papers we have counts for
7 papers · 1 filter
The Tempered Hilbert Simplex Distance and Its Application To Non-linear Embeddings of TEMs
Ehsan Amid, Frank Nielsen, Richard Nock +1
Tempered Exponential Measures (TEMs) are a parametric generalization of the exponential family of distributions maximizing the tempered entropy function among positive measures sub…
Heterogeneous Federated Learning Using Knowledge Codistillation
Jared Lichtarge, Ehsan Amid, Shankar Kumar +3
Federated Averaging, and many federated learning algorithm variants which build upon it, have a limitation: all clients must share the same model architecture. This results in unus…
Context-Aware Meta-Learning
Christopher Fifty, Dennis Duan, Ronald G. Junkins +4
Large Language Models like ChatGPT demonstrate a remarkable capacity to learn new concepts during inference without any fine-tuning. However, visual models trained to detect new ob…
Optimal Transport with Tempered Exponential Measures
Ehsan Amid, Frank Nielsen, Richard Nock +1
In the field of optimal transport, two prominent subfields face each other: (i) unregularized optimal transport, "à-la-Kantorovich", which leads to extremely sparse plans but with…
Boosting with Tempered Exponential Measures
Richard Nock, Ehsan Amid, Manfred K. Warmuth
One of the most popular ML algorithms, AdaBoost, can be derived from the dual of a relative entropy minimization problem subject to the fact that the positive weights on the exampl…
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…