June 6, 2024, 4:41 a.m. | Siavash Golkar, Alberto Bietti, Mariel Pettee, Michael Eickenberg, Miles Cranmer, Keiya Hirashima, Geraud Krawezik, Nicholas Lourie, Michael McCabe, R

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02585v1 Announce Type: new
Abstract: Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper introduces the contextual counting task, a novel toy problem aimed at enhancing our understanding of Transformers in quantitative and scientific contexts. This task requires precise localization and computation within datasets, akin to object detection or region-based scientific analysis. We present theoretical and empirical analysis using both causal and non-causal Transformer architectures, investigating the influence of …

abstract applications arxiv behavior cs.ai cs.lg diverse domains machine machine learning novel paper problem quantitative scientific stat.ml study transformers type understanding

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