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cs.LG2026
Algorithmic Task Capture, Computational Complexity, and Inductive Bias of Infinite Transformers
Orit Davidovich, Zohar Ringel
We formally define algorithmic capture of combinatorial tasks as the ability of a transformer to extrapolate to arbitrary task sizes with controllable error and logarithmic sample…
cs.LG2026
Mitigating the Curse of Detail: Scaling Arguments for Feature Learning and Sample Complexity
Noa Rubin, Orit Davidovich, Zohar Ringel
Two pressing topics in the theory of deep learning are the interpretation of feature learning (FL) mechanisms and the determination of implicit bias of networks in the rich regime.…
cs.LG2025
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
Nimrod Megiddo, Segev Wasserkrug, Orit Davidovich +1
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function va…