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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.NE2022★ 1 cited
Growing Isotropic Neural Cellular Automata
Alexander Mordvintsev, Ettore Randazzo, Craig Fouts
Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge…