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cs.LG2025
Transformative or Conservative? Conservation laws for ResNets and Transformers
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
While conservation laws in gradient flow training dynamics are well understood for (mostly shallow) ReLU and linear networks, their study remains largely unexplored for more practi…
cs.LG2024
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
Conservation laws are well-established in the context of Euclidean gradient flow dynamics, notably for linear or ReLU neural network training. Yet, their existence and principles f…
cs.LG2023
Sketch and shift: a robust decoder for compressive clustering
Ayoub Belhadji, Rémi Gribonval
Compressive learning is an emerging approach to drastically reduce the memory footprint of large-scale learning, by first summarizing a large dataset into a low-dimensional sketch…