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Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Publications associées (47)

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Transfer Learning by Sharing Support Vectors

Pascal Fua, Carlos Joaquin Becker, Vitaly Henry Ablavsky

Machine Learning techniques play an increasingly vital role in the analysis of Biomedical imagery, as in all other areas of Computer Vision. However, in this specific context, they suffer from the fact that experimental conditions and protocols change ofte ...

2012

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Jean-Philippe Thiran, Devis Tuia, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of n ...

2012

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Jean-Philippe Thiran, Volker Gass, Frank Grégoire Jean de Morsier, Maurice Borgeaud

Very often in change detection only few labels or even none are available. In order to perform change detection in these extreme scenarios, they can be considered as novelty detection problems, semi-supervised (SSND) if some labels are available otherwise ...

Ieee2012

Collaborative learning of mixture models using diffusion adaptation

Ali H. Sayed

In large ad-hoc networks, classification tasks such as spam filtering, multi-camera surveillance, and advertising have been traditionally implemented in a centralized manner by means of fusion centers. These centers receive and process the information that ...

IEEE2011

Geometry-aware analysis of high-dimensional visual information sets

Effrosyni Kokiopoulou

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009

Machine learning approaches to text representation using unlabeled data

Mikaela Keller

With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...

EPFL2008

Machine learning for information retrieval

David Grangier

In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...

EPFL2008

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Graph Chatbot

Chattez avec Graph Search

Transfer Learning by Sharing Support Vectors

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Collaborative learning of mixture models using diffusion adaptation

Geometry-aware analysis of high-dimensional visual information sets

Machine learning approaches to text representation using unlabeled data

Machine learning for information retrieval

Machine Learning for Information Retrieval

Machine Learning for Information Retrieval

Machine Learning Approaches to Text Representation using Unlabeled Data

Geometry-aware analysis of high-dimensional visual information sets

Machine learning approaches to text representation using unlabeled data

Machine learning for information retrieval

Machine Learning for Information Retrieval

Collaborative learning of mixture models using diffusion adaptation

Machine Learning for Information Retrieval

Machine Learning Approaches to Text Representation using Unlabeled Data

Semi-Supervised and Unsupervised Novelty Detection using Nested Support Vector Machines

Unsupervised Change Detection via Hierarchical Support Vector Clustering

Transfer Learning by Sharing Support Vectors