6 papers
Subzero matrix completion for sparse data analysis: large-scale learning of latent low-rank structure
Lawrence K. Saul, Ningyuan Huang, Dennis Bollweg +2
We investigate when a sparse nonnegative matrix can be recovered from a real-valued matrix of much lower rank by zeroing out its negative elements. The potential for such decomposi…
Attention to Mamba: A Recipe for Cross-Architecture Distillation
Abhinav Moudgil, Ningyuan Huang, Eeshan Gunesh Dhekane +3
State Space Models (SSMs) such as Mamba have become a popular alternative to Transformer models, due to their reduced memory consumption and higher throughput at generation compare…
Any-Subgroup Equivariant Networks via Symmetry Breaking
Abhinav Goel, Derek Lim, Hannah Lawrence +2
The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant archit…
World Guidance: World Modeling in Condition Space for Action Generation
Yue Su, Sijin Chen, Haixin Shi +7
Leveraging future observation modeling to facilitate action generation presents a promising avenue for enhancing the capabilities of Vision-Language-Action (VLA) models. However, e…
Multi-View Graph Learning with Graph-Tuple
Shiyu Chen, Ningyuan Huang, Soledad Villar
Graph Neural Networks (GNNs) typically scale with the number of graph edges, making them well suited for sparse graphs but less efficient on dense graphs, such as point clouds or m…
A Galois theorem for machine learning: Functions on symmetric matrices and point clouds via lightweight invariant features
Ben Blum-Smith, Ningyuan Huang, Marco Cuturi +1
In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by con…