Y. Zhang, T. Xiang, T. M. Hospedales, H. Lu. (2018). Deep Mutual Learning. Computer Vision and Pattern Recognition (CVPR).
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F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H.S. Torr, T. M. Hospedales. (2018). Learning to Compare: Relation Network for Few-Shot Learning. Computer Vision and Pattern Recognition (CVPR)
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D. Li, Y. Yang, Y.-Z. Song, T. M. Hospedales (2018) Learning to Generalize: Meta-Learning for Domain Generalization. AAAI Conference on Artificial Intelligence (AAAI)
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Colas, C., Sigaud, O. and Oudeyer, P.-Y. (2018) GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms, proceedings ICML.
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Fournier, P., Sigaud, O., Chetouani, M. and Oudeyer P.-Y. (2018) Accuracy-based Curriculum Learning in Deep Reinforcement Learning, arXiv preprint arXiv:1806.09614
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Sigaud, O. and Stulp, F. (2018) Policy Search in Continuous Action Domains: an Overview, arXiv preprint arXiv:1803.04706
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Lesort, T., Díaz-Rodríguez, N., Goudou, J.F., Filliat, D. (2018) State Representation Learning for Control: An Overview Neural Networks, Elsevier, 2018, 108, pp.379-392. doi: 10.1016/j.neunet.2018.07.006
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Cazé, R.* and Khamassi, M.* and Aubin, L. and Girard, B. (2018). Hippocampal replays under the scrutiny of reinforcement learning models. Journal of Neurophysiology. to appear (* equally contributing authors).
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Cazé, R., Stimberg, M., Girard, B. [Re] Non-additive coupling enables propagation of synchronous spiking activity in purely random networks. ReScience. Volume 4 number 1, 2018.
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Raffin, A., Hill, A., Traoré, R., Lesort, T., Dìaz-Rodrìguez, N., Filliat, D. (2018) S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning, NIPS 2018 Deep RL workshop
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Aubin, L., Khamassi, M., Girard, B. (2018). Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays. Proceedings of the Living Machines 2018 conference. doi: 10.1007/978-3-319-95972-6_4
Doncieux S, Filliat D, Díaz-Rodríguez N, Hospedales T, Duro R, Coninx A, Roijers DM, Girard B, Perrin N and Sigaud O (2018) Open-Ended Learning: A Conceptual Framework Based on Representational Redescription. Front. Neurorobot. 12:59. doi: 10.3389/fnbot.2018.00059
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Doncieux, S., & Coninx, A. (2018, July). Open-ended evolution with multi-containers QD. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 107-108). ACM.
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N. Bredeche, J.-M. Montanier, S. Carrignon (2017) Benefits of Proportionate Selection in Embodied Evolution: a Case Study with Behavioural Specialization. Evolving Collective Behaviors in Robotics Workshop at GECCO 2017, 2 pages. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO), pp.1683-1684.
N. Bredeche, E. Haasdijk, A. Prieto (2018) Embodied Evolution in Collective Robotics: A Review. Frontiers in Robotics and AI. vol. 5, 12 pages, DOI:10.3389/frobt.2018.00012, ISSN=2296-9144. 
A. Bernard, N. Bredeche, J-B. Andre. Individual selection leads to collective efficiency through coordination. doi:10.1101/422287


Y. Yang and T. M. Hospedales (2017) Deep Multi-task Representation Learning: A Tensor Factorisation Approach. International Conference on Learning Representations (ICLR).
C. Zhao, T. Hospedales, F. Stulp, O. Sigaud (2017) Tensor Based Knowledge Transfer Across Skill Categories for Robot Control. International Joint Conference on Artificial Intelligence (IJCAI).
Maestre, C., Mukhtar G. , Gonzales C. and Doncieux S. (2017) Iterative affordance learning with adaptive action generation. Proceedings ICDL 2017, Link to PDF.
Y. Yang, Y. Zheng, T. M. Hospedales (2017) Gated Neural Networks for Option Pricing: Rationality by Design. AAAI Conference on Artificial Intelligence. Link to PDF.
L. K. Le Goff, G. Mukhtar, P-H. Le Fur, S. Doncieux (2017) Segmenting objects through an autonomous agnostic exploration conducted by a robot. Proceedings ICRC 2017, April 10-12, 2017, Taichung, Taiwan,  pp.284-291. Link to PDF.
J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert , A.E. Eiben, T. Bartz-Beielstein and E. Haasdijk, Is Social Learning More Than Parameter Tuning?, GECCO 2017: Proceedings of The Genetic and Evolutionary Computation Conference, ACM, NY, 2017
J. Heinerman, J. Stork, M. A. R. Coy, J. Hubert , A.E. Eiben, T. Bartz-Beielstein and E. Haasdijk, Can Social Learning Increase Learning Speed, Performance or Both?, ECAL 2017: Proceedings of the Fourteenth European Conference on the Synthesis and Simulation of Living Systems, p. 200-207, MIT Press, 2017
E. Haasdijk and J. Heinerman, Quantifying Selection Pressure, Evolutionary Computation, In Press, 2017
J. Heinerman, E. Haasdijk, A.E. Eiben, Unsupervised identification and recognition of situations for high-dimensional sensori-motor streams, In Neurocomputing, Volume 262, 2017, Pages 90-107, ISSN 0925-2312, Link to PDF


Q. Yu, Y. Yang, F. Liu, Y-Z. Song, T. Xiang, T. Hospedales (2016) Sketch-a-Net: a Deep Neural Network that Beats Humans. International Journal of Computer Vision. doi: 10.1007/s11263-016-0932-3. Link to PDF.
Y. Zhou, T. M. Hospedales, N. Fenton (2016) When and Where to Transfer for Bayes Net Parameter Learning. Expert Systems with Applications. doi: http://dx.doi.org/10.1016/j.eswa.2016.02.011. Link to PDF.
Y. Yang, T. M. Hospedales (2016). Multivariate Regression on the Grassmannian for Predicting Novel Domains. IEEE Conference on Computer Vision and Pattern Recognition. Link to PDF.
Q. Yu, F. Liu, Y.-Z. Song, T. Xiang, T. M. Hospedales, C. C. Loy (2016). Sketch Me That Shoe. IEEE Conference on Computer Vision and Pattern Recognition. Link to PDF.
R. Salgado, A. Prieto, F. Bellas, L. Calvo-Varela, R.J. Duro (2016) Motivational engine with autonomous sub-goal identification for the Multilevel Darwinist Brain. Biologically Inspired Cognitive Architectures vol 17, pp 1-11, Elsevier. Link to PDF.
R. Salgado, A. Prieto, P. Caamaño, F. Bellas, R. J. Duro (2016) Motivational Engine with Sub-Goal Identification in Neuroevolution based Cognitive Robotics. Lecture Notes in Computer Science vol 9648, pp 659-670, Springer. Link to PDF.
R. Salgado, A. Prieto, F. Bellas, R. J. Duro (2016) Improving Extrinsically Motivated Developmental Robots through Intrinsic Motivations. Proceedings ICDL 2016. Link to PDF.
R. Salgado, A. Prieto, P. Caamaño, F. Bellas, R. J. Duro (2016) MotivEn: Motivational Engine with Sub-goal Identification for Autonomous Robots. Proceedings CEC 2016, IEEE Press. Link to PDF.
R. Duro, A. Prieto, J. A. Becerra, J. Monroy, P. Caamaño (2016) Considering Memory Networks in the LTM structure of the Multilevel Darwinist Brain. GECCO'16 Companion, July 20 - 24, 2016, Denver, ACMCO, USA. Link to PDF.
J. Heinerman, A. Zonta, E. Haasdijk, A.E. Eiben (2016) On-line Evolution of Foraging Behaviour in a Population of Real Robots. Applications of Evolutionary Computation. doi:10.1007/978-3-319-31153-1_14. Link to PDF.
Matricon A., Filliat D., Oudeyer P.-Y. (2016) An Iterative Algorithm for Forward-Parameterized Skill Discovery. IEEE International Conference on Development and Learning and on Epigenetic Robotics. Link to PDF.
J-M. Montanier, S. Carrignon, N. Bredeche (2016) Behavioural Specialisation in Embodied Evolutionary Robotics: Why so Difficult? Frontiers in Robotics and AI, Volume 3, number 38. Link to PDF.
A. Bernard, J.-B. André, N. Bredeche (2016) To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter. PLoS Computational Biology 12(5): e1004886. doi: 10.1371/journal.pcbi.1004886. Link to PDF.
A. Bernard, J.-B. André, N. Bredeche (2016) Evolving Specialisation in a Population of Heterogeneous Robots: the Challenge of Bootstrapping and Maintaining Genotypic Polymorphism. Proceedings of the 15th Conference on Simulation and Synthesis of Living Systems (ALIFE XV). Pages 1-8. Link to PDF.


Doncieux S., Bredeche N., Mouret J.-B., Eiben A.E. Evolutionary Robotics: What, Why, and Where to. Frontiers in Robotics and AI, Volume 2, number 4, 2015. doi: 10.3389/frobt.2015.00004. Link to PDF.

Salgado R., Bellas F., Duro R.J. Autonomous Learning of Procedural Knowledge in an Evolutionary Cognitive Architecture for Robots, EvoApplications 2015, LNCS 9028, pp. 807–818, 2015.

Trueba P., Prieto A., Bellas F., Duro R.J. Embodied Evolution for Collective Indoor Surveillance and Location. Bioinspired Computation in Artificial Systems. Lecture Notes in Computer Science, Volume 9108, 2015, pp 138-147. Link to PDF.

Salgado R., Bellas F., Duro R. J. Studying the Coupled Learning of Procedural and Declarative Knowledge in Cognitive Robotics. Biomimetic and Biohybrid Systems, LNCS Vol 9222, pp 304-315, 2015. doi: 10.1007/978-3-319-22979-9_30
Trueba P., Prieto A., Bellas F., Duro R.J. Applying the canonical distributed Embodied Evolution algorithm in a collective indoor navigation task. Neural Networks (IJCNN), 2015 International Joint Conference on , vol., no., pp.1-8, 12-17 July 2015, doi: 10.1109/IJCNN.2015.7280807

Heinerman J., Drupsteen D., Eiben A.E. Three-fold Adaptivity in Groups of Robots: The Effect of Social Learning. Proceedings of the 17th annual conference on Genetic and evolutionary computation, 2015. doi: 10.1145/2739480.2754743. Link to PDF.

Heinerman, J., Rango, M., Eiben A.E. Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots. In IEEE International Conference on Evolvable Systems (ICES),  to appear in 2015.

Bernard A., André J.-B., Bredeche N. Evolution of Cooperation in Evolutionary Robotics: The Tradeoff Between Evolvability and Efficiency. To be published in the proceedings of the European Conference on Artificial Life 2015. 8 pages. Link to PDF.

Zhou Y., Fenton, N., Hospedales, T. M., Neil. M. Probabilistic Graphical Models Parameter Learning with Transferred Prior and Constraints. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI) 2015. Link to PDF.

Stulp F., Sigaud S. Many Regression Algorithms, One Unified Model - A Review. Neural Networks, Volume 69, 2015. Link to PDF.

Palminteri S., Khamassi M., Joffily M., Coricelli G. Contextual modulation of value signals in reward and punishment learning. Nature Communications, 6:8096, 2015. Link.

Legoff, L. and Maestre, C. and Doncieux, S. (2015). Visual saliency-based babbling of unknown dynamic environments. Proceedings of the workshop Learning Object Affordances, IROS 2015. Link to PDF.

Ecarlat, P. and Cully, A. and Maestre, C. and Doncieux, S. (2015). Learning a high diversity of object manipulations though an evolutionary-based babbling. Proceedings of the workshop Learning Object Affordances, IROS 2015. Link to PDF.

Maestre, C. and Cully, A. and Gonzales, C. and Doncieux, S. (2015). Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach. IEEE International Conference on Developmental and Learning and on Epigenetic Robotics. Link to PDF.

Q. Yu, Y. Yang, Y-Z. Song, T. Xiang and T. Hospedales. Sketch-a-Net that Beats Humans. British Machine Vision Conference (BMVC). 2015. Link to PDF.

G. Hu, Y. Yang, D. Yi, J. Kittler, W. Christmas, S. Z. Li, T. Hospedales. When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition. ICCV Workshop ChaLearn Looking at People. 2015. Link to PDF

Stulp F., Grizou J., Busch B., and Lopes M. Facilitating Intention Prediction for Humans by Optimizing Robot Motions. Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems (IROS), 2015. Link to PDF.

Sigaud O., Masson C., Filliat D., Stulp F. Gated networks: an inventory. arXiv preprint arXiv:1512.03201. Link to PDF.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640891 (DREAM project)


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