| Efficient neural audio synthesis N Kalchbrenner, E Elsen, K Simonyan, S Noury, N Casagrande, ... International Conference on Machine Learning, 2410-2419, 2018 | 1040 | 2018 |
| 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 | 1014 | 2018 |
| 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 | 338 | 2021 |
| 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 | 295 | 2021 |
| 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 | 290 | 2021 |
| 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 | 231 | 2021 |
| 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 | 189 | 2018 |
| 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 | 49 | 2021 |
| 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 | 32 | 2017 |
| 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 | 16 | 2020 |
| 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 | 16 | 2012 |
| Inference in continuous-time change-point models F Stimberg, M Opper, G Sanguinetti, A Ruttor Advances in Neural Information Processing Systems 24, 2011 | 16 | 2011 |
| Imagen 3 J Baldridge, J Bauer, M Bhutani, N Brichtova, A Bunner, K Chan, Y Chen, ... arXiv preprint arXiv:2408.07009, 2024 | 9 | 2024 |
| 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 | 8 | 2023 |
| 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 | 7 | 2014 |
| 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 | 2 | 2021 |
| 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 | 2 | 2021 |
| Flexible birth-death MCMC sampler for changepoint models F Stimberg PQDT-Global, 2016 | 1 | 2016 |
| 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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