Concept

Defeasible reasoning

Related publications (25)

Monotonicity Reasoning in the Age of Neural Foundation Models

The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our unde ...

Dordrecht2023

Reshaping Perception for Autonomous Driving with Semantic Keypoints

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The field of artificial intelligence is set to fuel the future of mobility by driving forward the transition from advanced driver-assist systems to fully autonomous vehicles (AV). Yet the current technology, backed by cutting-edge deep learning techniques, ...

EPFL2022

Person Retrieval in Surveillance Videos Via Deep Attribute Mining and Reasoning

Zhen Wei, Zhiye Wang, Peixia Li

Person retrieval largely relies on the appearance features of pedestrians. This task is rather more difficult in surveillance videos due to the limitations of extracting robust appearance features brought by the cross-view and cross-camera data with lower ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

CauseOccam: Learning Interpretable Abstract Representations in Reinforcement Learning Environments via Model Sparsity

Sergei Volodin

"I choose this restaurant because they have vegan sandwiches" could be a typical explanation we would expect from a human. However, current Reinforcement Learning (RL) techniques are not able to provide such explanations, when trained on raw pixels. RL alg ...

2021

Contextual semantic interpretability

Devis Tuia, Sylvain Lobry, Nicolas Courty

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in t ...

LNCS2020

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

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

Electricity supply in West Africa: between bilateral agreements and a single regional market

It is necessary to specify that this study is an academic work carried out by a student as part of his master's project. The hypotheses and reasoning made by the student are at the origin of the results obtained. Energy has a crucial role in the economic d ...

2019

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-Verlag Berlin2015

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