4 papers
Dissipative Learning: A Framework for Viable Adaptive Systems
Laurent Caraffa
We propose a perspective in which learning is an intrinsically dissipative process. Forgetting and regularization are not heuristic add-ons but structural requirements for adaptive…
Thermodynamically Optimal Regularization under Information-Geometric Constraints
Laurent Caraffa
Modern machine learning relies on a collection of empirically successful but theoretically heterogeneous regularization techniques, such as weight decay, dropout, and exponential m…
BEDS : Bayesian Emergent Dissipative Structures : A Formal Framework for Continuous Inference Under Energy Constraints
Laurent Caraffa
We introduce BEDS (Bayesian Emergent Dissipative Structures), a formal framework for analyzing inference systems that must maintain beliefs continuously under energy constraints. U…
Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis
Thang-Anh-Quan Nguyen, Nathan Piasco, Luis Roldão +5
Synthesizing extrapolated views remains a difficult task, especially in urban driving scenes, where the only reliable sources of data are limited RGB captures and sparse LiDAR poin…