Cognitive Machine Learning

An interdisciplinary research team at the intersection of cognitive and computer sciences


CoML is based in the Departement d’Etudes Cognitives of the Ecole Normale Supérieure and is a joint EHESS, ENS, CNRS and Inria research team at the intersection between cognitive and computer science.

Brain-inspired machine learning algorithms combined with big data have recently reached spectacular results, beating humans on specific high level tasks (e.g. the game of go). However, there are still a lot of domains in which even humans infants outperform machines: unsupervised learning of rules and language, common sense reasoning, and more generally, cognitive flexibility (the ability to quickly transfer competence from one domain to another one).

The aim of the Cognitive Machine Learning team is to reverse engineer such human abilities, i.e., to construct effective and scalable algorithms which perform as well (or better) than humans, when provided with similar data, study their mathematical and algorithmic properties and test their empirical validity as models of humans by comparing their output with behavioral and neuroscientific data. The expected results are more adaptable and autonomous machine learning algorithm for complex tasks, and quantitative models of cognitive processes which can used to predict human developmental and processing data. Most of the work is focused on speech and language and common sense reasoning.

Research Themes

We work mainly in two research areas.

Reverse engineering early language acquisition. We develop unsupervised learning algorithms which discover speech and language units from raw sensory data (speech and video of parent/infant interactions) and validate them empirically in infants.

Human/Machine Benchmarking. We construct open-source cognitive tests designed to evaluate how an artificial system performs a complex function like language processing or reasoning and compare results with human performance on the same task and data.


The CoML research team is supported and funded by the Ecole des Hautes Etudes en Sciences Sociales, the Ecole Normale Superieure which are both part of PSL Research University, and two national research institutes: the Centre National de la Recherche Scientifique and Inria. It is a part of the Laboratoire de Sciences Cognitives et Psycholinguistique (UMR 8554), a research unit of the Cognitive Studies Departement, a thriving research department of the Ecole Normale Superieure at the heart of the quartier latin in Paris. In short (!), the team’s affiliation is:

EHESS, ENS, PSL Research University and CNRS.



The team’s project is funded by the European Research Council (ERC-2011-AdG-295810 BOOTPHON), the Agence Nationale pour la Recherche (ANR-17-EURE-0017 Frontcog, ANR-10-IDEX-0001-02 PSL*, ANR-19-P3IA-0001 PRAIRIE 3IA Institute) and grants from CIFAR (Learning in Machines and Brains), Facebook AI Research (Research Grant), Google (Faculty Research Award), Microsoft Research (Azure Credits and Grant), and Amazon Web Service (AWS Research Credits).

ERC ANR FAIR Google Microsoft

The team’s PhD students are funded by the Ecole Doctorale Cerveau Cognition Comportement, the Ecole Doctorale Frontières du Vivant, the Interdisciplinary Doctoral School (ED540), the Ecole de Neurosciences de Paris, the Ecole Normale Supérieure, the INRIA, the Direction Générale de l’Armement or the Association Nationale Recherche Technologie (CIFRE Phd Grants).

ed3c Frontières du vivant ed540 ENP ENS Inria DGA ANRT