25 citations · 50 across the 5 of their papers we have counts for
5 papers
Graph Neural Networks with Adaptive Readouts
David Buterez, Jon Paul Janet, Steven J. Kiddle +2
An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. T…
Towards Robust Waveform-Based Acoustic Models
Dino Oglic, Zoran Cvetkovic, Peter Sollich +2
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions. This problem is of pa…
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…
Scalable Learning in Reproducing Kernel Krein Spaces
Dino Oglic, Thomas Gärtner
We provide the first mathematically complete derivation of the Nyström method for low-rank approximation of indefinite kernels and propose an efficient method for finding an approx…
Towards A Unified Analysis of Random Fourier Features
Zhu Li, Jean-Francois Ton, Dino Oglic +1
Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused…