Giambattista Parascandolo

According to our database, Giambattista Parascandolo authored at least 21 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2023

Predicting Ordinary Differential Equations with Transformers.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

Proceedings of the International Conference on Machine Learning, 2023

2022

Discovering ordinary differential equations that govern time-series.

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo, Niki Kilbertus

CoRR, 2022

Explanatory Learning: Beyond Empiricism in Neural Networks.

Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli, Giambattista Parascandolo, Simone Melzi, Emanuele Rodolà

CoRR, 2022

2021

Deep Learning Beyond the Training Distribution.

Giambattista Parascandolo

PhD thesis, 2021

Neural Symbolic Regression that scales.

Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning explanations that are hard to vary.

Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf

Proceedings of the 9th International Conference on Learning Representations, 2021

A teacher-student framework to distill future trajectories.

Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning.

Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber

CoRR, 2020

2018

Generalization in anti-causal learning.

Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf

CoRR, 2018

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models.

Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Independent Causal Mechanisms.

Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf

Proceedings of the 35th International Conference on Machine Learning, 2018

Tempered Adversarial Networks.

Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection.

Emre Çakir, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen

IEEE ACM Trans. Audio Speech Lang. Process., 2017

Low latency sound source separation using convolutional recurrent neural networks.

Gaurav Naithani, Tom Barker, Giambattista Parascandolo, Lars Bramslow, Niels Henrik Pontoppidan, Tuomas Virtanen

Proceedings of the 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2017

Avoiding Discrimination through Causal Reasoning.

Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A convolutional neural network approach for acoustic scene classification.

Michele Valenti, Stefano Squartini, Aleksandr Diment, Giambattista Parascandolo, Tuomas Virtanen

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Convolutional recurrent neural networks for bird audio detection.

Emre Cakir, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos, Tuomas Virtanen

Proceedings of the 25th European Signal Processing Conference, 2017

2016

Recurrent neural networks for polyphonic sound event detection in real life recordings.

Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

Low-latency sound source separation using deep neural networks.

Gaurav Naithani, Giambattista Parascandolo, Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

DCASE 2016 Acoustic Scene Classification Using Convolutional Neural Networks.

Michele Valenti, Aleksandr Diment, Giambattista Parascandolo, Stefano Squartini, Tuomas Virtanen

Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events, 2016

Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features.

Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen

Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events, 2016