Hugging Face Trending Papers
TPR-Attention for Combinatorial Generalization
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arXiv AI
Sep 1
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 Machine Learning
Sep 22
Disassociating performance from compositional feature learning
arXiv:2505.09716v4 Announce Type: replace Abstract: Out-of-distribution (OOD) generalisation through composition requires a system to discover invariant properties from input-output associations and...
arXiv AI
Sep 10
A Theoretical Analysis of Provable Compositional Generalization in Neural Networks: A Necessary and Sufficient Condition
The paper presents a necessary and sufficient condition for provable compositional generalization in neural networks, identifying two key principles: structural alignment and unambiguous minimized representations. It rigorously proves this condition, verifies it in Lean 4, and demonstrates its applicability in few-shot settings, including the SCAN jump task. The authors also outline an algorithmic approach and illustrate it with a minimal example, all derived purely from mathematical analysis without empirical validation.
arXiv AI
Oct 2
What Drives Compositional Generalization in Visual Generative Models? The Importance of Continuous Training Objectives
arXiv:2510.03075v4 Announce Type: replace-cross Abstract: Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models....
arXiv Machine Learning
Oct 22
Memorizing Long-tail Data Can Help Generalization Through Composition
The paper investigates how memorizing long‑tail data can aid generalization when combined with a model’s ability to compose features. It demonstrates theoretically that, in a linear setting, memorization plus composition enables correct predictions on rare test examples that involve unseen combinations of long‑tail features. Experiments with neural networks on simple datasets confirm that this effect extends beyond linear models and that a model’s compositional capability depends on its architecture.
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.