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Showing papers from Ecole Nationale des Ponts et Chausees Show all papers

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NeurIPS 2026Google DeepMindGoogleEcole Nationale des Ponts et ChaAutoML & architecture search

Regularized Large Neighborhood Search

Regularized LNS turns local search heuristics into MCMC samplers with Fenchel-Young losses, enabling exact block Gibbs sampling and end-to-end learning without global solvers.

Germain Vivier-Ardisson, Laurent Demonet, Axel Parmentier, Mathieu Blondel

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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lenient 3/5
medium 6/10
strict 1/5
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NeurIPS 2026Google DeepMindGoogleEcole Nationale des Ponts et ChaAutoML & architecture search

Differentiable Knapsack and Top-k Operators via Dynamic Programming

A unified framework casts knapsack and top-k operators as dynamic programs with smoothed recursions for differentiable relaxations, parallel algorithms, and theoretical regularization guarantees.

Germain Vivier-Ardisson, Michael E Sander, Axel Parmentier, Mathieu Blondel

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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lenient 4/5
medium 6/10
strict 2/5
76%Highly rated
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NeurIPS 2026École Nationale des Ponts ParisTENPCEcole Nationale des Ponts et ChaENPC ParisTechPose & motion

Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video

A primitive-fitting framework recovers articulated object kinematics from single casual videos via joint optimization of part segmentation and joint parameters under occlusions.

Arslan Artykov, Tom Ravaud, Nicolás Violante, Vincent Lepetit

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 10 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 0/5