Publications associées à Programmation logique inductive

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. Altho ...

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

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 ...

2021

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

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

A novel biomechanical approach for animal behaviour recognition using accelerometers

Kamiar Aminian, Pritish Chakravarty

Data from animal-borne inertial sensors are widely used to investigate several aspects of an animal's life, such as energy expenditure, daily activity patterns and behaviour. Accelerometer data used in conjunction with machine learning algorithms have b ...

2019

Perceptual Quality Study On Deep Learning Based Image Compression

Touradj Ebrahimi, Pinar Akyazi

Recently deep learning based image compression has made rapid advances with promising results based on objective quality metrics. However, a rigorous subjective quality evaluation on such compression schemes have rarely been reported. This paper aims at pe ...

IEEE2019

Energy management in manufacturing: From literature review to a conceptual framework

Dimitrios Kyritsis, Gökan May

The literature on energy management in manufacturing is rapidly growing. Since the literature is also quite fragmented, we believe that the time has come to delve into current knowledge in the research field to provide directions for policy makers and guid ...

Elsevier2017

Optimizing Majority-Inverter Graphs with Functional Hashing

Giovanni De Micheli, Mathias Soeken, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù

A Majority-Inverter Graph (MIG) is a recently introduced logic representation form whose algebraic and Boolean properties allow for efficient logic optimization. In particular, when considering logic depth reduction, MIG algorithms obtained signific ...

IEEE2016

Induction for SMT Solvers

Viktor Kuncak, Andrew Joseph Reynolds

Satisfiability modulo theory solvers are increasingly being used to solve quantified formulas over structures such as integers and term algebras. Quantifier instantiation combined with ground decision procedure alone is insufficient to prove many formulas ...

Springer-Verlag2015

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

Linear Bayesian Reinforcement Learning

Christos Dimitrakakis, Nikolaos Tziortziotis

This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurately estimating the system dynamics, and in particular when we allow for corre ...

2013

Unsupervised adaptation for acceleration-based activity recognition: Robustness to sensor displacement and rotation

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the pot ...

Springer Verlag2013

Qualitative Spatial and Temporal Reasoning with Answer Set Programming

Jingshi Li

Representing and reasoning spatial and temporal information is a key research issue in Computer Science and Artificial Intelligence. In this paper, we introduce tools that produce three novel encodings which translate problems in qualitative spatial and te ...

2012

Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

Measurement of Tc distribution in NbSn CICC

Pierluigi Bruzzone, Davide Uglietti, Ciro Calzolaio

Knowledge of the actual Nb3Sn filaments' thermal strain, epsilon(th), in a cable-in-conduit conductor (CICC) is essential to predict the CICC performance in operation starting from the strand scaling laws for the critical current density J(c)(B, T, epsilon ...

SUST 20122012

In situ Tc measurements of cable in conduit conductors via an inductive method

Pierluigi Bruzzone, Davide Uglietti, Boris Stepanov, Ciro Calzolaio

The measurement of the actual thermal strain, epsilon th, in a cable in conduit conductor (CICC) of Nb3Sn is of paramount importance to predict the CICC performance in operation starting from the strand scaling laws for the critical current density J(c)(B, ...

IEEE-Inst Electrical Electronics Engineers Inc2012

Energy Harvesting and Remote Powering for Implantable Biosensors

Giovanni De Micheli, Sandro Carrara, Jacopo Olivo

The paper reviews some popular techniques to harvest energy for implantable biosensors. For each technique, the advantages and drawbacks are discussed. Emphasis is placed to the inductive links, able to deliver power wirelessly through the biological tissu ...

Institute of Electrical and Electronics Engineers2011