Topic: Adaptive sampling

One of the most important questions in psychology and neuroscience is understanding how the outside world maps to internal representations. Classical psychophysics approaches to this problem have a number of limitations: they mostly study low dimensional perpetual spaces, and are constrained in the number and diversity of participants and experiments. As ecologically valid perception is rich, high dimensional, contextual, and culturally dependent, these impediments severely bias our understanding of perceptual representations. Recent technological advances—the emergence of so-called “Virtual L

At extreme scale, even conventional sampling and projection methods can become too expensive. This talk treats locality-sensitive hashing as an amortized constant-time adaptive sampler, connecting probabilistic hash tables with efficient unbiased statistical estimators. A few hash lookups can support adaptive estimation at a cost close to uniform sampling. Applications include partition-function estimation for large natural-language models such as word2vec, adaptive gradient estimation for stochastic gradient descent, and sublinear deep learning with very large parameter spaces. The talk also