Publications associées à Raisonnement à partir de cas

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

REFINER: Reasoning Feedback on Intermediate Representations

Boi Faltings, Robert West, Maxime Jean Julien Peyrard, Antoine Bosselut, Beatriz Maria Borges Ribeiro, Debjit Paul, Mahammad Ismayilzada

Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, these intermediate inference steps may be in- appropriate deductions from the i ...

2023

Language Model Decoding as Likelihood–Utility Alignment

Boi Faltings, Robert West, Maxime Jean Julien Peyrard, Martin Josifoski, Valentin Hartmann, Debjit Paul, Jiheng Wei, Frano Rajic

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re- main unclear. Previous works only compare decoding algorithms in narrow sc ...

Assoc Computational Linguistics-Acl2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque, Tianfu Li, Qi Li, Baorui Dai

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng, Jinwei Chen

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas ...

MDPI2022

Cognitive twin construction for system of systems operation based on semantic integration and high-level architecture

Dimitrios Kyritsis, Jinzhi Lu, Han Li

With the increasing complexity of engineered systems, digital twins (DTs) have been widely used to support integrated modeling, simulation, and decision-making of the system of systems (SoS). However, when integrating DTs of each constituent system, it is ...

IOS PRESS2022

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

EPFL2022

Scaling Language Features for Program Verification

Georg Stefan Schmid

Formal verification of real-world software systems remains challenging for a number of reasons, including lack of automation, friction in specifying properties, and limited support for the diverse programming paradigms used in industry. In this thesis we m ...

EPFL2022

Continual Test-Time Domain Adaptation

Olga Fink, Qin Wang

Test-time domain adaptation aims to adapt a source pretrained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems are running in n ...

IEEE COMPUTER SOC2022

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

Stoic: Towards Disciplined Capabilities

Martin Odersky, Nada Amin, Fengyun Liu, Sandro Stucki, Paolo Giosuè Giarrusso

Capabilities are widely used in the design of software systems to ensure security. A system of capabilities can become a mess in the presence of objects and functions: objects may leak capabilities and functions may capture capabilities. They make reasonin ...

2020

Biomimetic adaptive control of a deployable tensegrity structure

Ann Christine Sychterz

Biomimetic behavior includes aspects such as learning from previous experience, self-diagnosis, and adaptation. This thesis describes control methodologies that are essential to development towards biomimetic behavior of a complex deployable structure. Sim ...

EPFL2018

Miniphases: Compilation using Modular and Efficient Tree Transformations

Martin Odersky, Dmytro Petrashko, Ondrej Lhoták

Production compilers commonly perform dozens of transformations on an intermediate representation. Running those transformations in separate passes harms performance. One approach to recover performance is to combine transformations by hand in order to red ...

Assoc Computing Machinery2017

From Nano- to Micrometer Scale: The Role of Antisolvent Treatment on High Performance Perovskite Solar Cells

Mohammad Khaja Nazeeruddin, Francesco Stellacci, Giulia Grancini, Pascal Alexander Schouwink, Yi Zhang, Peng Gao, Sanghyun Paek, Kyung Taek Cho, Evangelia-Nefeli Athanasopoulou

To date, antisolvent treatment has become one of the most important means to fabricate high efficiency perovskite solar cells (PSCs); however, the few reported antisolvents have not been analyzed on a uniform platform, and there is hitherto no clear reason ...

American Chemical Society2017

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

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Learning Search Strategies from Human Demonstrations

Guillaume Pierre Luc De Chambrier

Decision making and planning with partial state information is a problem faced by all forms of intelligent entities. The formulation of a problem under partial state information leads to an exorbitant set of choices with associated probabilistic outcomes m ...

EPFL2016

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

Scalable and Reactive Programming for Semantic Web Developers

Jean Paul Calbimonte Perez

The Semantic Web brings a powerful set of concepts, standards and ideas that are already changing the shape of the Web. However, in order to put these notions into practice we need to translate them into code. That is why the broad notion of programming th ...

CEUR-WS2015

Viability evolutionary algorithms and applications to neuroscience and biology

Andrea Maesani

Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals ...

EPFL2015