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Concept

Connexionnisme

Publications associées (15)

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Matteo Monti, Andrei Kucharavy

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

Assoc Computing Machinery2023

Kernel Memory Networks: A Unifying Framework for Memory Modeling

Wulfram Gerstner, Johanni Michael Brea, Georgios Iatropoulos

We consider the problem of training a neural network to store a set of patterns with maximal noise robustness. A solution, in terms of optimal weights and state update rules, is derived by training each individual neuron to perform either kernel classifica ...

Curran Associates2022

Strong temperature-dependent thermoelectric power of IrMn(3)thin films of different thicknesses

Jean-Philippe Ansermet, Haiming Yu, Junfeng Hu

IrMn(3)thin films were experimentally characterized by thermoelectric power and resistivity as a function of temperature and sample thickness. The Seebeck coefficient shows strong temperature dependence and the value increases with temperature up to 350 K. ...

IOP PUBLISHING LTD2021

Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

Robert West, Alberto Garcia Duran, Eda Bayram

The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, d ...

2021

Model Fusion via Optimal Transport

Martin Jaggi, Sidak Pal Singh

Combining different models is a widely used paradigm in machine learning applications. While the most common approach is to form an ensemble of models and average their individual predictions, this approach is often rendered infeasible by given resource co ...

2020

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

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny

We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outlie ...

2018

Learning to Find Good Correspondences

Pascal Fua, Mathieu Salzmann, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny

IEEE2018

Non-intrusive reduced order modeling of nonlinear problems using neural networks

Jan Sickmann Hesthaven

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial differential equations (PDEs). The method extracts a reduced basis from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD) and emplo ...

2018

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Martin Jaggi, Matteo Pagliardini, Prakhar Gupta

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...

2017

Elicitation and Modeling of Operational Human Decision Making in a Supply Chain Context : a Study Based on Participatory Simulation

Souleïman Naciri

Supply chains (SC) are complex systems in which human beings play a key role. Whereas human impact is often taken into account in simulation models in terms of physical flows (material handling, fabrication), it is far less the case in terms of information ...

EPFL2010

Networks of Mixed Canonic-Dissipative Systems and Dynamic Hebbian Learning

Max-Olivier Hongler, Julio Rodriguez

We consider a collection {O_k}_{k=1}^N of interacting parametric mixed canonical-dissipative systems, (MCD). Each individual Ok, exhibits, in absence of interaction, a limit cycle L_k on which the orbit circulation is parameterized by w_k(t). The underlyin ...

Shaker Verlag2008

Systematic encoding and decoding of chain reaction codes

Mohammad Amin Shokrollahi

A method of encoding data into a chain reaction code includes generating a set of input symbols from input data. Subsequently, one or more non-systematic output symbols is generated from the set of input symbols, each of the one or more non-systematic outp ...

2005

Bayesian Networks Based Multimodality Fusion for Error Handling in Human-Robot Dialogues Under Noisy Conditions

Andrzej Drygajlo, Plamen Prodanov

In this paper, we introduce probabilistic model based architecture for error handling in human–robot spoken dialogue systems under adverse audio conditions. In this architecture, a Bayesian network framework is used for interpretation of multi-modal signal ...

2005

Optimization of high order perceptrons

Neural networks are widely applied in research and industry. However, their broader application is hampered by various technical details. Among these details are several training parameters and the choice of the topology of the network. The subject of this ...

EPFL1997