Követés
Florian Stimberg
Florian Stimberg
DeepMind
E-mail megerősítve itt: google.com
Cím
Hivatkozott rá
Hivatkozott rá
Év
Efficient neural audio synthesis
N Kalchbrenner, E Elsen, K Simonyan, S Noury, N Casagrande, ...
International Conference on Machine Learning, 2410-2419, 2018
10372018
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
10142018
Data augmentation can improve robustness
SA Rebuffi, S Gowal, DA Calian, F Stimberg, O Wiles, TA Mann
Advances in Neural Information Processing Systems 34, 29935-29948, 2021
3372021
Improving robustness using generated data
S Gowal, SA Rebuffi, O Wiles, F Stimberg, DA Calian, TA Mann
Advances in Neural Information Processing Systems 34, 4218-4233, 2021
2952021
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
2902021
A fine-grained analysis on distribution shift
O Wiles, S Gowal, F Stimberg, S Alvise-Rebuffi, I Ktena, K Dvijotham, ...
arXiv preprint arXiv:2110.11328, 2021
2312021
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
1892018
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
492021
Heiga Zen, Alex Graves, Helen King, Tom Walters, Dan Belov, and Demis Hassabis
A Van Den Oord, Y Li, I Babuschkin, K Simonyan, O Vinyals, ...
Parallel wavenet: Fast high-fidelity speech synthesis. CoRR, abs/1711.10433, 2017
322017
WaveNetEQ—Packet loss concealment with WaveRNN
F Stimberg, A Narest, A Bazzica, L Kolmodin, PB Gonzalez, O Sharonova, ...
2020 54th Asilomar Conference on Signals, Systems, and Computers, 672-676, 2020
162020
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
162012
Inference in continuous-time change-point models
F Stimberg, M Opper, G Sanguinetti, A Ruttor
Advances in Neural Information Processing Systems 24, 2011
162011
Imagen 3
J Baldridge, J Bauer, M Bhutani, N Brichtova, A Bunner, K Chan, Y Chen, ...
arXiv preprint arXiv:2408.07009, 2024
92024
Benchmarking robustness to adversarial image obfuscations
F Stimberg, A Chakrabarti, CT Lu, H Hazimeh, O Stretcu, W Qiao, Y Liu, ...
Advances in Neural Information Processing Systems 36, 42830-42865, 2023
82023
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 27, 2014
72014
A fine-grained analysis of robustness to distribution shifts
O Wiles, S Gowal, F Stimberg, SA Rebuffi, I Ktena, KD Dvijotham, ...
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
22021
Doing more with less: Improving robustness using generated data
S Gowal, SA Rebuffi, O Wiles, F Stimberg, D Calian, T Mann, L DeepMind
ICLR Workshop on Security and Safety in Machine Learning Systems, 2021
22021
Flexible birth-death MCMC sampler for changepoint models
F Stimberg
PQDT-Global, 2016
12016
Verifying the provenance of a digital object using watermarking and embeddings
SA Gowal, C Gamble, FN Stimberg, SAG Rebuffi, SM Thotakuri, J Hayes, ...
US Patent 12,094,474, 2024
2024
Documentation of the SwitchSampler Program Version 1.0
F Stimberg
2016
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Cikkek 1–20