Giambattista Parascandolo
Giambattista Parascandolo
PhD Student, Max Planck Institute for Intelligent Systems & ETH Zurich
Verified email at tue.mpg.de - Homepage
Title
Cited by
Cited by
Year
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
4002017
Avoiding Discrimination through Causal Reasoning
N Kilbertus, M Rojas-Carulla, G Parascandolo, M Hardt, D Janzing, ...
NIPS 2017, 2017
3692017
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
3062016
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
1182017
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
712017
Learning Independent Causal Mechanisms
G Parascandolo, M Rojas-Carulla, N Kilbertus, B Schölkopf
Proceedings of the 35th International Conference on Machine Learning (ICML), 2018
662018
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
662017
Taming the waves: sine as activation function in deep neural networks
G Parascandolo, H Huttunen, T Virtanen
272016
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
252017
Generalization in anti-causal learning
N Kilbertus*, G Parascandolo*, B Schölkopf*
NeurIPS 2018 Workshop on Critiquing and Correcting Trends in Machine Learning, 2018
222018
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
A Neitz, G Parascandolo, S Bauer, B Schölkopf
Neural Information Processing Systems (NIPS), 2018
212018
Learning explanations that are hard to vary
G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf
ICLR 2021, 2021
202021
Tempered Adversarial Networks
MSM Sajjadi, G Parascandolo, A Mehrjou, B Schölkopf
Proceedings of the 35th International Conference on Machine Learning (ICML), 2018
152018
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
132016
Divide-and-Conquer Monte Carlo Tree Search
G Parascandolo, LH Buesing, J Merel, L Hasenclever, J Aslanides, ...
12*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
122017
A Seq2Seq approach to Symbolic Regression
L Biggio, T Bendinelli, A Lucchi, G Parascandolo
NeurIPS 2020 Workshop "Knowledge Representation & Reasoning Meets Machine …, 2020
22020
Neural Symbolic Regression that Scales
L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo
ICML 2021, 2021
2021
A teacher-student framework to distill future trajectories
A Neitz, G Parascandolo, B Schölkopf
ICLR 2021, 2021
2021
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Articles 1–19