| Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection E Cakir*, G Parascandolo*, T Heittola, H Huttunen, T Virtanen IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (6), 1291 …, 2017 | 482 | 2017 |
| Avoiding Discrimination through Causal Reasoning N Kilbertus, M Rojas-Carulla, G Parascandolo, M Hardt, D Janzing, ... NIPS 2017, 2017 | 468 | 2017 |
| Recurrent neural networks for polyphonic sound event detection in real life recordings G Parascandolo, H Huttunen, T Virtanen Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International …, 2016 | 355 | 2016 |
| DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks. M Valenti, A Diment, G Parascandolo, S Squartini, T Virtanen DCASE, 95-99, 2016 | 160 | 2016 |
| Sound event detection in multichannel audio using spatial and harmonic features S Adavanne, G Parascandolo, P Pertilä, T Heittola, T Virtanen arXiv preprint arXiv:1706.02293, 2017 | 127 | 2017 |
| Learning Independent Causal Mechanisms G Parascandolo, M Rojas-Carulla, N Kilbertus, B Schölkopf Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 | 107 | 2018 |
| Convolutional recurrent neural networks for bird audio detection E Cakir, S Adavanne, G Parascandolo, K Drossos, T Virtanen 2017 25th European Signal Processing Conference (EUSIPCO), 1744-1748, 2017 | 91 | 2017 |
| A convolutional neural network approach for acoustic scene classification M Valenti, S Squartini, A Diment, G Parascandolo, T Virtanen 2017 International Joint Conference on Neural Networks (IJCNN), 1547-1554, 2017 | 84 | 2017 |
| Learning explanations that are hard to vary G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf ICLR 2021, 2021 | 49 | 2021 |
| Taming the waves: sine as activation function in deep neural networks G Parascandolo, H Huttunen, T Virtanen | 41 | 2016 |
| Generalization in anti-causal learning N Kilbertus*, G Parascandolo*, B Schölkopf* NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018 | 32 | 2018 |
| Low latency sound source separation using convolutional recurrent neural networks G Naithani, T Barker, G Parascandolo, L Bramsl, NH Pontoppidan, ... 2017 IEEE Workshop on Applications of Signal Processing to Audio and …, 2017 | 32 | 2017 |
| Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models A Neitz, G Parascandolo, S Bauer, B Schölkopf Neural Information Processing Systems (NIPS), 2018 | 28 | 2018 |
| Tempered Adversarial Networks MSM Sajjadi, G Parascandolo, A Mehrjou, B Schölkopf Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 | 26 | 2018 |
| Low-latency sound source separation using deep neural networks G Naithani, G Parascandolo, T Barker, NH Pontoppidan, T Virtanen 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2016 | 16 | 2016 |
| Divide-and-Conquer Monte Carlo Tree Search G Parascandolo, LH Buesing, J Merel, L Hasenclever, J Aslanides, ... | 15* | 2020 |
| CONVWAVE: Searching for Gravitational Waves with Fully Convolutional Neural Nets T Gebhard, N Kilbertus, G Parascandolo, I Harry, B Schölkopf Workshop on Deep Learning for Physical Sciences, NIPS 2017, 2017 | 14 | 2017 |
| Neural Symbolic Regression that Scales L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo ICML 2021, 2021 | 12 | 2021 |
| A Seq2Seq approach to Symbolic Regression L Biggio, T Bendinelli, A Lucchi, G Parascandolo NeurIPS 2020 Workshop "Knowledge Representation & Reasoning Meets Machine …, 2020 | 4 | 2020 |
| Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1 | 2022 |
Tuomas VirtanenTampere UniversityVerified email at tuni.fi
