Connectionists: Postdoc: Machine Learning for Modeling Multi-relational Data (Compiègne, France)

[antoine.bordes at hds.utc.fr]

[apologies if you receive multiple copies of this advert]

**Mixtures of Three-way Models for Multi-relational Data.**

**Supervision** Antoine Bordes, CNRS - Université de Technologie de Compiègne.

**Dates** position open from January 1st, 2014. (earlier or later  
start dates can be negotiable)

**Project description**
Many data such as Knowledge Bases  (e.g. freebase.com) are  
multi-relational, in that they describe multiple relations between  
entities. While there is a large body of work focused on modeling  
these data, modeling these multiple types of relations jointly remains  
challenging.  This research project targets data with large numbers of  
relation types (more than 10k), and for which the various relation  
types have heterogeneous properties like different connectivities or  
occurrence frequencies for instance. We propose to take this into  
account by using different relational three-way models (e.g. variants  
of tensor factorization models, with different loss functions,  
architectures, constraints, etc.) for different relation types. These  
models would be trained jointly and share some parameters (e.g. those  
encoding the entities), leading to an overall mixture of three-way  
models.

Such models will be based on previous work by Bordes et al. on  
modeling multi-relational data (see the publication page at  
https://www.hds.utc.fr/everest/doku.php?id=en:publications for recent  
papers) and will be developed in collaboration with Google.

**Context**
A post-doctoral position is available as part of a Google Reseach  
Award obtained by Antoine Bordes. Research will be conducted within  
the French ANR project EVEREST on "lEarning high-leVEl REpresentations  
of large Sparse Tensors" being undertaken by Heudiasyc laboratory in  
Université de Technologie de Compiègne, with a partnership of Xerox  
Research Center Europe (Grenoble, France). See  
https://www.hds.utc.fr/everest for more details on the project.

The post-doctoral fellow will be based in the Heudiasyc laboratory in  
Compiègne (France -- 1h north of Paris) and join the DI team headed by  
Yves Grandvalet. He/she will be supervised by Antoine Bordes.  
Heudiasyc is a joint laboratory with the Université de Technologie de  
Compiègne (UTC) and the French governmental agency for research  
(CNRS). In 2011, it was rated A+ (the highest rate) by the French  
Research evaluation agency (AERES). Heudiasyc fosters  
interdisciplinary research on information science and technology  
including machine learning, uncertain reasoning, operations research,  
robotics and knowledge management. In 2011 Heudiasyc was awarded with  
an excellence project (LabEx) on the « Control of Technological  
Systems of Systems ».

The fellowship is funded through a Google Research Award and will  
start after January 1st, 2014 for one year (currently 2500? per month  
-- gross salary).

**Requirements**
The candidate should have a PhD or equivalent in computer science or  
mathematics. The following qualities are desirable : strong interests  
in machine learning, statistics or natural language processing;  
excellent record of academic and/or professional achievement; strong  
mathematical skills; strong programming skills ; good written and  
spoken communication skills in French or English. The ideal candidate  
should be able to conduct theoretical research, but also implement and  
test models on very large  datasets.

**Contact and application**
Applicants should send (preferably as a single PDF file):
   * a CV
   * a brief statement of research interests
   * references (with email and phone number)
   * a sample of strongest publications

Applications and inquiries should be directed to: Antoine Bordes -  
antoine.bordes at hds.utc.fr - https://www.hds.utc.fr/~bordesan