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Podcast: Machine Learning Street Talk (MLST)
Episode: #69 DR. THOMAS LUX - Interpolation of Sparse High-Dimensional Data
Description: Today we are speaking with Dr. Thomas Lux, a research scientist at Meta in Silicon Valley.Â
In some sense, all of supervised machine learning can be framed through the lens of geometry. All training data exists as points in euclidean space, and we want to predict the value of a function at all those points. Neural networks appear to be the modus operandi these days for many domains of prediction. In that light; we might ask ourselves — what makes neural networks better than classical techniques like K nearest neighbour from a geometric perspective. Our gue...