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Spike-and-slab regression is a standard Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are active rather than a single selected subset, so that every candidate effect carries an inclusion probability. Its cost grows exponentially with the number of candidate...
It is argued that locality and hierarchy are useful priors for learning field representations that can be injected without compromising modality-agnosticism in LH-NeF, a framework to learn general-purpose tokenized representations of continuous signals.
Minimizing gradients of a convex function is an important problem across optimization and learning tasks. The gradient provides a directly computable certificate of approximate stationarity, and its minimization usually implies stronger results than those for minimization of function values. In this work, we study grad...
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