Publications associées à Monotonicity Reasoning in the Age of Neural Foundation Models

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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

Overcoming Multi-model Forgetting

Mathieu Salzmann, Anthony Christopher Davison, Martin Jaggi, Yassine Benyahia, Claudiu-Cristian Musat, Kaicheng Yu

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

JMLR2019

Computational memory-based inference and training of deep neural networks

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing is an emerging computing paradigm where certain computational tasks are performed in place in a computational memory unit by exploiting the physical attributes of the memory devices, Here, we present an overview of the application of in ...

IEEE2019

Evolution of Neural Network Architectures for Speech Recognition

Hervé Bourlard

Over these last few years, the use of Artificial Neural Networks (ANNs), now often referred to as deep learning or Deep Neural Networks (DNNs), has significantly reshaped research and development in a variety of signal and information processing tasks. Whi ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard, Youngjoo Seo

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary grap ...

2017

metaSMT: focus on your application and not on solver integration

Mathias Soeken, Heinz Riener

Many applications from artificial intelligence and formal methods use decision procedures as their core solving engines. In this context, automated reasoning based on Satisfiability (SAT) or Satisfiability Modulo Theories (SMT) is very effective. For a giv ...

Springer Verlag2017

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

A Decision Procedure for (Co)datatypes in SMT Solvers

Andrew Joseph Reynolds

We present a decision procedure that combines reasoning about datatypes and codatatypes. The dual of the acyclicity rule for datatypes is a uniqueness rule that identifies observationally equal codatatype values, including cyclic values. The procedure deci ...

Springer-Verlag Berlin2015

Monotonicity Reasoning in the Age of Neural Foundation Models

Graph Chatbot

Chattez avec Graph Search

Contextual semantic interpretability

Overcoming Multi-model Forgetting

Computational memory-based inference and training of deep neural networks

Evolution of Neural Network Architectures for Speech Recognition

Deep Learning Works in Practice. But Does it Work in Theory?

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

metaSMT: focus on your application and not on solver integration

Word Sequence Modeling using Deep Learning

Induction for SMT Solvers

A Decision Procedure for (Co)datatypes in SMT Solvers

Evolution of Neural Network Architectures for Speech Recognition

Computational memory-based inference and training of deep neural networks

Word Sequence Modeling using Deep Learning

metaSMT: focus on your application and not on solver integration

Deep Learning Works in Practice. But Does it Work in Theory?

Induction for SMT Solvers

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

A Decision Procedure for (Co)datatypes in SMT Solvers

Contextual semantic interpretability

Overcoming Multi-model Forgetting