Search Results for author: Giambattista Parascandolo

Found 16 papers, 8 papers with code

Explanatory Learning: Beyond Empiricism in Neural Networks

1 code implementation25 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.

Program Synthesis

Neural Symbolic Regression that Scales

1 code implementation11 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.

Divide-and-Conquer Monte Carlo Tree Search

no code implementations1 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.

Continuous Control Decision Making

Learning to interpret trajectories

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.

Learning explanations that are hard to vary

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.

Generalization in anti-causal learning

no code implementations3 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.

Tempered Adversarial Networks

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.

Learning Independent Causal Mechanisms

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.

Transfer Learning

Avoiding Discrimination through Causal Reasoning

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.

Fairness Frame

Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features

no code implementations7 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.

Event Detection Sound Event Detection

Convolutional Recurrent Neural Networks for Bird Audio Detection

no code implementations7 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.

Bird Audio Detection

Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection

1 code implementation21 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.

Event Detection Sound Event Detection

Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings

2 code implementations4 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).

Data Augmentation Event Detection +1

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