2 citations · 2 across the 5 of their papers we have counts for
5 papers
Memory-Modular Classification: Learning to Generalize with Memory Replacement
Dahyun Kang, Ahmet Iscen, Eunchan Jo +3
We propose a novel memory-modular learner for image classification that separates knowledge memorization from reasoning. Our model enables effective generalization to new classes b…
Prediction-Powered Causal Inferences
Riccardo Cadei, Ilker Demirel, Piersilvio De Bartolomeis +4
In many scientific experiments, the data annotating cost constraints the pace for testing novel hypotheses. Yet, modern machine learning pipelines offer a promising solution, provi…
Smoke and Mirrors in Causal Downstream Tasks
Riccardo Cadei, Lukas Lindorfer, Sylvia Cremer +2
Machine Learning and AI have the potential to transform data-driven scientific discovery, enabling accurate predictions for several scientific phenomena. As many scientific questio…
Learning Correlation Structures for Vision Transformers
Manjin Kim, Paul Hongsuck Seo, Cordelia Schmid +1
We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attent…
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering
Juhong Min, Shyamal Buch, Arsha Nagrani +2
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework. Previous modular methods have shown promise with a si…