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Yann LeCun

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#machine learning Preprint Sep 2026

Learning Functional Subspaces for Neural Network Compression

Modern transformers pair impressive capabilities with substantial memory and compute demands. Low-rank weight factorization reduces both while keeping the matrices dense, and thus efficient on standard hardware. Existing methods, however, choose the subspace to remove from each weight matrix with local closed-form crit...

Massimo Bini, Anders Christensen, S. Alaniz et al. · 0 citations
#machine learning Preprint Sep 2026

SCOPE: Observation-Conditioned Full-Target Prediction for Sparse PDE Inference

Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Diffusion-based PDE solvers address this problem through iterative sampling whereas neural operators provide deterministic one-pass predictions. We propose SCOPE (Sparse-...

Rui-Chen Xu, Si-Yao Wang, Fang Wan et al. · 0 citations
#machine learning Preprint Aug 2026

HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input reconstruction by predicting masked targets directly in latent space. However, existing graph JEPAs ty...

Ruichen Xu, Jingxiang Qu, Wenhan Gao et al. · 0 citations

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