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cs.LG2022★ 89 cited
Transformers learn in-context by gradient descent
Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo +4
At present, the mechanisms of in-context learning in Transformers are not well understood and remain mostly an intuition. In this paper, we suggest that training Transformers on au…
cs.LG2021★ 1 cited
NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
Alexander Mordvintsev, Eyvind Niklasson
We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative proces…