Publications associées à Catastrophic interference

Fast adaptation to rule switching using neuronal surprise

Wulfram Gerstner, Martin Louis Lucien Rémy Barry

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

2024

Stable parameterization of continuous and piecewise-linear functions

Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

2023

Probabilistic methods for neural combinatorial optimization

Nikolaos Karalias

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

EPFL2023

Expectation consistency for calibration of neural networks

Florent Gérard Krzakala, Lenka Zdeborová, Lucas Andry Clarte, Bruno Loureiro, Bruno Loureiro

Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour tow ...

2023

ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation

Boi Faltings, Fei Mi, Xiaoyu Lin

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, ...

ACM2022

Controllability and Interpretability in Affective Speech Synthesis

Bastian Schnell

Thanks to Deep Learning Text-To-Speech (TTS) has achieved high audio quality with large databases. But at the same time the complex models lost any ability to control or interpret the generation process. For the big challenge of affective TTS it is infeasi ...

EPFL2022

Approximating Relu Networks By Single-Spike Computation

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Ana Stanojevic, Evangelos Eleftheriou, Giovanni Cherubini, Angeliki Pantazi

Developing energy-saving neural network models is a topic of rapidly increasing interest in the artificial intelligence community. Spiking neural networks (SNNs) are biologically inspired models that strive to leverage the energy efficiency stemming from a ...

IEEE2022

Natural-Looking Adversarial Examples From Freehand Sketches

Davide Nanni, Hakgu Kim

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

IEEE2022

Adversarial training with informed data selection

Pascal Frossard, Javier Alejandro Maroto Morales, Marcele Oliveira Kuhfuss de Mendonça

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of ...

IEEE2022

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

Boi Faltings, Mengjie Zhao, Fei Mi, Liangwei Chen

Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applicat ...

2021