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stat.ML2019★ 8 cited
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural Networks
Dino Oglic, Zoran Cvetkovic, Peter Sollich
We investigate the potential of stochastic neural networks for learning effective waveform-based acoustic models. The waveform-based setting, inherent to fully end-to-end speech re…
stat.ML2012★ 2 cited
Random walk kernels and learning curves for Gaussian process regression on random graphs
Matthew Urry, Peter Sollich
We consider learning on graphs, guided by kernels that encode similarity between vertices. Our focus is on random walk kernels, the analogues of squared exponential kernels in Eucl…
stat.ML2012★ 4 cited
Replica theory for learning curves for Gaussian processes on random graphs
Matthew J. Urry, Peter Sollich
Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learni…