Hugging Face Trending Papers
TPR-Attention for Combinatorial Generalization
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
6d ago
TPR-Attention for Combinatorial Generalization
The paper introduces TPR-Attention, an attention mechanism that operates over tensor‑product representations to embed structured inductive bias into deep learning models. Experiments on compositional tasks demonstrate that TPR‑Attention outperforms existing architectural components in achieving combinatorial generalization. The results suggest that incorporating explicit compositional structure into neural attention can improve systematic generalization.
arXiv AI
Jul 3
A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention
arXiv:2601. 04509v2 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard to solve at scale due to NP-hardness.
arXiv Machine Learning
Jun 30
To Use or not to Use Muon: How Simplicity Bias in Optimizers Matters
arXiv:2603. 00742v2 Announce Type: replace Abstract: While Adam has long been the ubiquitous default optimizer for deep neural networks, Muon has recently seen rapid adoption due to its superior training speed.
arXiv AI
Jul 28
Variational-Ising-Attention (VIA):TailoredAttentionMattersfor Science
arXiv:2607. 23634v1 Announce Type: cross Abstract: Attention enables context modeling via query-key scoring with softmax normalization.
arXiv Machine Learning
Jul 7
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
arXiv:2602. 24264v2 Announce Type: replace-cross Abstract: Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems.
Hugging Face Trending Papers
Jun 22
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?