Florian Stimberg
Florian Stimberg
DeepMind
Bestätigte E-Mail-Adresse bei google.com
Titel
Zitiert von
Zitiert von
Jahr
Parallel wavenet: Fast high-fidelity speech synthesis
A Oord, Y Li, I Babuschkin, K Simonyan, O Vinyals, K Kavukcuoglu, ...
International conference on machine learning, 3918-3926, 2018
5662018
Efficient neural audio synthesis
N Kalchbrenner, E Elsen, K Simonyan, S Noury, N Casagrande, ...
International Conference on Machine Learning, 2410-2419, 2018
4912018
Wavenet based low rate speech coding
WB Kleijn, FSC Lim, A Luebs, J Skoglund, F Stimberg, Q Wang, ...
2018 IEEE international conference on acoustics, speech and signal …, 2018
832018
Inference in continuous-time change-point models
F Stimberg, M Opper, G Sanguinetti, A Ruttor
Advances in Neural Information Processing Systems 24, 2717-2725, 2011
182011
Fixing data augmentation to improve adversarial robustness
SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, T Mann
arXiv preprint arXiv:2103.01946, 2021
132021
Bayesian inference for change points in dynamical systems with reusable states-a chinese restaurant process approach
F Stimberg, A Ruttor, M Opper
Artificial Intelligence and Statistics, 1117-1124, 2012
132012
Poisson process jumping between an unknown number of rates: application to neural spike data
F Stimberg, A Ruttor, M Opper
Advances in Neural Information Processing Systems, 730-738, 2014
42014
Defending Against Image Corruptions Through Adversarial Augmentations
DA Calian, F Stimberg, O Wiles, SA Rebuffi, A Gyorgy, T Mann, S Gowal
arXiv preprint arXiv:2104.01086, 2021
22021
WaveNetEQ—Packet Loss Concealment with WaveRNN
F Stimberg, A Narest, A Bazzica, L Kolmodin, PB González, O Sharonova, ...
2020 54th Asilomar Conference on Signals, Systems, and Computers, 672-676, 2020
12020
Bayesian Inference for Models of Transcriptional Regulation Using Markov Chain Monte Carlo Sampling
F Stimberg, A Ruttor, M Opper
Proceedings of the 8th International Workshop on Computational Systems …, 2011
12011
Flexible birth-death MCMC sampler for changepoint models
F Stimberg
PQDT-Global, 2016
2016
Comparing diffusion and weak noise approximations for inference in reaction models
A Ruttor, F Stimberg, M Opper
Machine Learning in Systems Biology, 149, 2010
2010
Comparing Markov Chain Monte Carlo Proposal Densities for Diffusion Processes
F Stimberg
Technische Universität Berlin, 2010
2010
DOING MORE WITH LESS: IMPROVING ROBUSTNESS USING GENERATED DATA
S Gowal, SA Rebuffi, O Wiles, F Stimberg, D Calian, T Mann, L DeepMind
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