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Explore a 55-minute conference talk by Cengiz Pehlevan from Harvard University, presented at IPAM's Theory and Practice of Deep Learning Workshop. Delve into two stories of mechanistic interpretation in natural and artificial neural computation. Examine the remarkable ability of Transformers to perform in-context learning (ICL) without explicit prior training. Investigate an exactly solvable model of ICL for linear regression tasks using linear attention, uncovering sharp asymptotics for the learning curve in a scaling regime with infinite token dimension. Discover a double-descent learning curve and a phase transition between low and high task diversity regimes, revealing insights into memorization versus genuine in-context learning and generalization. Validate theoretical findings through experiments with both linear attention and full nonlinear Transformer architectures.
