arXiv Machine Learning
Disassociating performance from compositional feature learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Sep 15
Dissociating performance from compositional feature learning
arXiv:2505.09716v3 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) generalisation is considered a hallmark of human and animal intelligence. To achieve OOD through composition, a sys...
arXiv Machine Learning
Aug 4
LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks
arXiv:2511. 12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations.
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.
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?
Hugging Face Trending Papers
Aug 31
TPR-Attention for Combinatorial Generalization
Systematic generalization remains a significant challenge in deep learning. In particular, combinatorial generalization - generalizing to new configurations of known factors of variation - is effortle...
arXiv Computer Vision
Sep 28
Diagnosing the Sources of Compositional Failure in Vision-Language Models: A Controlled Analysis
arXiv:2609.31456v1 Announce Type: new Abstract: Vision-language models (VLMs) often struggle with compositional reasoning tasks, but the reasons for this underperformance remain unclear. A common hyp...