Vous êtes ici : Accueil » Kiosque » Annonce

Identification

Identifiant: 
Mot de passe : 

Mot de passe oublié ?
Détails d'identification oubliés ?

Annonce

4 juin 2017

ICCV International Workshop on Manifold Learning: from Euclid to Riemann, ICCV 2017


Catégorie : Conférence internationale


In this workshop, we will explore the latest development in machine learning techniques developed to work on/benefit from the non-linear manifolds. We will also target challenges and future directions related to the application of non-linear geometry, Riemannian manifolds in computer vision and machine learning. This workshop also acts as an opportunity for cross-disciplinary discussions and collaborations.

IMPORTANT DATES:

  • Paper Submission: June 15th , 2017
  • Author Notification: July 31th 2017
  • Camera Ready: August 25th , 2017
  • Workshop: October 28th, 2017

ICCV International Workshop onManifold Learning: from Euclid to Riemann, ICCV 2017

Venice, Italy - 28 October 2017

In conjunction with ICCV 2017

Webpage: https://sites.google.com/site/maniflearn/

Contact: maniflearn@googlegroups.com

IMPORTANT DATES

CALL FOR PAPERS

Manifold Learning (ML) has been the subject of intensive study over the past two decades in the computer vision and machine learning communities. Originally, manifold learning techniques aim to identify the underlying structure (usually low-dimensional) of data from a set of observations (in the form of high-dimensional vectors). The recent advances in deep learning make one wonder whether data-driven learning techniques can benefit from the theoretical findings from ML studies. This innocent looking question becomes more important if we note that deep learning techniques are notorious for being data-hungry and supervised (mostly). On the contrary, many ML techniques unravel data structures without much supervision. This workshop considers itself as the frontier to raise the question of how classical ML techniques can help deep learning and vice versa and targets studies and discussions that bridge the gap.

In this workshop, we will explore the latest development in machine learning techniques developed to work on/benefit from the non-linear manifolds. We will also target challenges and future directions related to the application of non-linear geometry, Riemannian manifolds in computer vision and machine learning. This workshop also acts as an opportunity for cross-disciplinary discussions and collaborations.

We encourage discussions on recent advances, ongoing developments, and novel applications of manifold learning, optimization, feature representations and deep learning techniques. We are soliciting original contributions that address a wide range of theoretical and practical issues including, but not limited to:

INVITED SPEAKERS

WORKSHOP CHAIRS

SUBMISSION AND REVISION

All submissions will be handled electronically via the conference’s CMT Website (https://cmt3.research.microsoft.com/MANIFLEARN2017). Submitted papers must be no longer than 8 pages following the official ICCV guideline http://iccv2017.thecvf.com/submission/main_conference/author_guidelines.ManifLearn reviewing will be double-blind. Each submission will be reviewed by at least three reviewers for originality, significance, clarity, soundness, relevance and technical contents. Papers that are not blind, or do not use the template, or have more than 8 pages (excluding references) will be rejected without review.

 

Dans cette rubrique

(c) GdR 720 ISIS - CNRS - 2011-2015.