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Podcast: Machine Learning Street Talk (MLST)
Episode: Jonas Hübotter (ETH) - Test Time Inference
Description: Jonas Hübotter, PhD student at ETH Zurich's Institute for Machine Learning, discusses his groundbreaking research on test-time computation and local learning. He demonstrates how smaller models can outperform larger ones by 30x through strategic test-time computation and introduces a novel paradigm combining inductive and transductive learning approaches.
Using Bayesian linear regression as a surrogate model for uncertainty estimation, Jonas explains how models can efficiently adapt to specific tasks without massive pre-training. He draws an analogy to Google Earth's variable resolution system to illustrate dynamic resource allocation based on task complexity.