582 citations · 728 across the 16 of their papers we have counts for
19 papers
VertAX: a differentiable vertex model for learning epithelial tissue mechanics
Alessandro Pasqui, Jim Martin Catacora Ocana, Anshuman Sinha +7
Epithelial tissues dynamically reshape through local mechanical interactions among cells, a process well captured by vertex models. Yet their many tunable parameters make inference…
Learning long range dependencies through time reversal symmetry breaking
Guillaume Pourcel, Maxence Ernoult
Deep State Space Models (SSMs) reignite physics-grounded compute paradigms, as RNNs could natively be embodied into dynamical systems. This calls for dedicated learning algorithms…
Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
Guillaume Pourcel, Debabrota Basu, Maxence Ernoult +1
Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational description of their fixed points. Ext…
Towards training digitally-tied analog blocks via hybrid gradient computation
Timothy Nest, Maxence Ernoult
Power efficiency is plateauing in the standard digital electronics realm such that novel hardware, models, and algorithms are needed to reduce the costs of AI training. The combina…
Energy-based learning algorithms for analog computing: a comparative study
Benjamin Scellier, Maxence Ernoult, Jack Kendall +1
Energy-based learning algorithms have recently gained a surge of interest due to their compatibility with analog (post-digital) hardware. Existing algorithms include contrastive le…
Towards Scaling Difference Target Propagation by Learning Backprop Targets
Maxence Ernoult, Fabrice Normandin, Abhinav Moudgil +5
The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to scale-up to real-world tasks, limiting…