1 code implementation • 25 Jan 2022 • Antonio Norelli, Giorgio Mariani, Luca Moschella, Andrea Santilli, Giambattista Parascandolo, Simone Melzi, Emanuele Rodolà
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e. g. explanations written in hieroglyphic -- by autonomously learning to interpret them.
1 code implementation • 11 Jun 2021 • Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo
We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
no code implementations • 1 Jan 2021 • Giambattista Parascandolo, Lars Holger Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
no code implementations • ICLR 2021 • Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf
By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience.
1 code implementation • NeurIPS Workshop LMCA 2020 • Luca Biggio, Tommaso Bendinelli, Aurelien Lucchi, Giambattista Parascandolo
Deep neural networks have proved to be powerful function approximators.
3 code implementations • ICLR 2021 • Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning.
no code implementations • 23 Apr 2020 • Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
no code implementations • 3 Dec 2018 • Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf
Anti-causal models are used to drive this search, but a causal model is required for validation.
2 code implementations • NeurIPS 2018 • Alexander Neitz, Giambattista Parascandolo, Stefan Bauer, Bernhard Schölkopf
We introduce a method which enables a recurrent dynamics model to be temporally abstract.
no code implementations • ICML 2018 • Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf
A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.
1 code implementation • ICML 2018 • Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf
The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization.
no code implementations • NeurIPS 2017 • Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, Bernhard Schölkopf
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.
no code implementations • 7 Jun 2017 • Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task.
no code implementations • 7 Mar 2017 • EmreÇakır, Sharath Adavanne, Giambattista Parascandolo, Konstantinos Drossos, Tuomas Virtanen
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions.
1 code implementation • 21 Feb 2017 • Emre Çakır, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure.
2 code implementations • 4 Apr 2016 • Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).