24 citations · 37 across the 5 of their papers we have counts for
3 papers · 1 filter
Kernel computations from large-scale random features obtained by Optical Processing Units
Ruben Ohana, Jonas Wacker, Jonathan Dong +4
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation. However, performing random projection…
Efficient Approximate Inference with Walsh-Hadamard Variational Inference
Simone Rossi, Sebastien Marmin, Maurizio Filippone
Variational inference offers scalable and flexible tools to tackle intractable Bayesian inference of modern statistical models like Bayesian neural networks and Gaussian processes.…
Walsh-Hadamard Variational Inference for Bayesian Deep Learning
Simone Rossi, Sebastien Marmin, Maurizio Filippone
Over-parameterized models, such as DeepNets and ConvNets, form a class of models that are routinely adopted in a wide variety of applications, and for which Bayesian inference is d…