Concept

Programmation logique inductive

Publications associées (38)

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...

EPFL2024

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Olga Fink, Raffael Pascal Theiler, Michele Viscione

Deep-learning-based digital twins (DDT) are a promising tool for data-driven system health management because they can be trained directly on operational data. A major challenge for efficient training however is that industrial datasets remain unlabeled. T ...

Research Publishing2023

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Jiancheng Yang, Zheng Dang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Los Alamitos2023

Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions

Olga Fink, Jian Zhou

Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic d ...

Research Publishing2023

Some Properties of the Kilbas-Saigo Function

Thomas Simon, Lotfi Boudabsa

We characterize the complete monotonicity of the Kilbas-Saigo function on the negative half-line. We also provide the exact asymptotics at -infinity, and uniform hyperbolic bounds are derived. The same questions are addressed for the classical Le Roy funct ...

MDPI2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order t ...

EPFL2021

A Bayesian Model of Individual Differences and Flexibility in Inductive Reasoning for Categorization of Examples

Pierre Dillenbourg, Jennifer Kaitlyn Olsen, Louis Pierre Faucon

Inductive reasoning is an important educational practice but can be difficult for teachers to support in the classroom due to the high level of preparation and classroom time needed to choose the teaching materials that challenge students' current views. I ...

ASSOC COMPUTING MACHINERY2020

Empirical validation of MesoHABSIM models developed with different habitat suitability criteria for bullhead cottus gobio L. as an indicator species

Giovanni De Cesare, Mikolaj Stanislaw Adamczyk, Piotr Parasiewicz

Application of instream habitat models such as the Mesohabitat Simulation Model (MesoHABSIM) is becoming increasingly popular. Such models can predict alteration to a river physical habitat caused by hydropower operation or river training. They are a tool ...

2019

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

Immanuel Chee Beng Koh

The spatial and formal conception of architecture, and thus its modes of design perception and representation, directly contributes to its machine-learnability; and consequently, its capacity in leveraging today's machine learning apparatus for design inno ...

EPFL2019

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