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Giambattista Parascandolo
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Books and Theses
- 2021
- [b1]Giambattista Parascandolo:
Deep Learning Beyond the Training Distribution. ETH Zurich, Zürich, Switzerland, 2021
Journal Articles
- 2017
- [j1]Emre Çakir, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen:
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection. IEEE ACM Trans. Audio Speech Lang. Process. 25(6): 1291-1303 (2017)
Conference and Workshop Papers
- 2023
- [c16]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. ICML 2023: 1978-2002 - 2021
- [c15]Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf:
A teacher-student framework to distill future trajectories. ICLR 2021 - [c14]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. ICLR 2021 - [c13]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that scales. ICML 2021: 936-945 - 2018
- [c12]Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf:
Tempered Adversarial Networks. ICLR (Workshop) 2018 - [c11]Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. ICML 2018: 4033-4041 - [c10]Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf:
Tempered Adversarial Networks. ICML 2018: 4448-4456 - [c9]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. NeurIPS 2018: 9838-9848 - 2017
- [c8]Emre Cakir, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos, Tuomas Virtanen:
Convolutional recurrent neural networks for bird audio detection. EUSIPCO 2017: 1744-1748 - [c7]Michele Valenti, Stefano Squartini, Aleksandr Diment, Giambattista Parascandolo, Tuomas Virtanen:
A convolutional neural network approach for acoustic scene classification. IJCNN 2017: 1547-1554 - [c6]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. NIPS 2017: 656-666 - [c5]Gaurav Naithani, Tom Barker, Giambattista Parascandolo, Lars Bramslow, Niels Henrik Pontoppidan, Tuomas Virtanen:
Low latency sound source separation using convolutional recurrent neural networks. WASPAA 2017: 71-75 - 2016
- [c4]Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen:
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features. DCASE 2016: 6-10 - [c3]Michele Valenti, Aleksandr Diment, Giambattista Parascandolo, Stefano Squartini, Tuomas Virtanen:
DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks. DCASE 2016: 95-99 - [c2]Gaurav Naithani, Giambattista Parascandolo, Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen:
Low-latency sound source separation using deep neural networks. GlobalSIP 2016: 272-276 - [c1]Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen:
Recurrent neural networks for polyphonic sound event detection in real life recordings. ICASSP 2016: 6440-6444
Informal and Other Publications
- 2023
- [i14]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Predicting Ordinary Differential Equations with Transformers. CoRR abs/2307.12617 (2023) - 2022
- [i13]Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli, Giambattista Parascandolo, Simone Melzi, Emanuele Rodolà:
Explanatory Learning: Beyond Empiricism in Neural Networks. CoRR abs/2201.10222 (2022) - [i12]Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus:
Discovering ordinary differential equations that govern time-series. CoRR abs/2211.02830 (2022) - 2021
- [i11]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that Scales. CoRR abs/2106.06427 (2021) - 2020
- [i10]Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber:
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning. CoRR abs/2004.11410 (2020) - [i9]Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf:
Learning explanations that are hard to vary. CoRR abs/2009.00329 (2020) - 2018
- [i8]Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf:
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models. CoRR abs/1808.04768 (2018) - [i7]Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf:
Generalization in anti-causal learning. CoRR abs/1812.00524 (2018) - 2017
- [i6]Emre Çakir, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen:
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection. CoRR abs/1702.06286 (2017) - [i5]Emre Çakir, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos, Tuomas Virtanen:
Convolutional Recurrent Neural Networks for Bird Audio Detection. CoRR abs/1703.02317 (2017) - [i4]Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen:
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features. CoRR abs/1706.02293 (2017) - [i3]Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf:
Avoiding Discrimination through Causal Reasoning. CoRR abs/1706.02744 (2017) - [i2]Giambattista Parascandolo, Mateo Rojas-Carulla, Niki Kilbertus, Bernhard Schölkopf:
Learning Independent Causal Mechanisms. CoRR abs/1712.00961 (2017) - 2016
- [i1]Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen:
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings. CoRR abs/1604.00861 (2016)
Coauthor Index
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