My objective is to understand the fundamental learning mechanisms that underpin the development of high-level visual representations and behaviors in humans and machines. My approach is to construct bio-inspired models based on powerful machine learning approaches; this includes self-supervised representation learning, intrinsic motivation, hierarchical reinforcement learning. I currently work as a postdoctoral researcher at the Frankfurt Institute for Advanced Studies (FIAS).
Feel free to reach out if you wish to talk !
Below is my list of papers:
Postdoc, Frankfurt Institute for Advanced Studies: Toddler-inspired object representation learning
Aubret, Arthur and Jochen Triesch. Do vision models perceive objects like toddlers ? In ICLR Blogposts 2025. temporary link
Aubret, Arthur, Céline Teulière, and Jochen Triesch. Seeing the Whole in the Parts in Self-Supervised Representation Learning. Under review. paper
Zhengyang Yu, Aubret, Arthur, Marcel C Raabe, Jane Yang, Chen Yu, and Jochen Triesch. Active gaze behavior boosts self-supervised object learning. Under review. paper
Aubret, A.*, Schaumlöffel, T.*, Roig, G., & Triesch, J. (2025). Human Gaze Boosts Object-Centered Representation Learning. Under review. paper
Aubret, A., Teulière, C., & Triesch, J. (2024). Self-supervised visual learning from interactions with objects. The European Conference on Computer Vision, 2024. github, paper
Aubret, A.*, Schaumlöffel, T.*, Roig, G., & Triesch, J. (2024). Learning Object Semantic Similarity with Self-Supervision. In 2024 IEEE International Conference on Development and Learning. IEEE. paper
Ernst, M. R., López, F. M., Aubret, A., Fleming, R. W., & Triesch, J. (2024). Self-Supervised Learning of Color Constancy. In 2024 IEEE International Conference on Development and Learning. IEEE. github, paper
Schaumlöffel, T.*, Aubret, A.*, Roig, G., & Triesch, J. (2023, November). Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play. In 2023 IEEE International Conference on Development and Learning. IEEE. paper
Postdoc, Clermont Ferrand Pascal institute: Toddler-inspired object representation learning
Aubret, A.*, Ernst, M. R.*, Teulière, C., & Triesch, J. Time to augment self-supervised visual representation learning. In The Eleventh International Conference on Learning Representations, 2023. paper
Arthur Aubret, Céline Teulière, and Jochen Triesch. Embodied vision for learning object representations. Joint IEEE International Conference on Development and Learning (ICDL), 2022. paper
Arthur Aubret, Céline Teulière, and Jochen Triesch. Toddler-inspired learning induces hierarchical object representations. 3th Sensorimotor Interaction, Language and Embodiment of Symbols (SMILES) workshop at ICDL, 2022. paper
Aubret, A., Lefort, M., Teulière, C., Matignon, L., Hassas, S., & Triesch, J. Compressed information is all you need: unifying intrinsic motivations and representation learning. In NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems, 2022. paper
Dominik Mattern, Francisco M. López, Markus R. Ernst, Arthur Aubret, and Jochen Triesch. Mimo: A multi-modal infant model for studying cognitive development in humans and ais. Joint IEEE International Conference on Development and Learning (ICDL), 2022. paper
Ph.D, Lyon, LIRIS: Learning increasingly complex skills through deep reinforcement learning using intrinsic motivation
Aubret, A., Matignon, L., & Hassas, S. (2023). An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey. Entropy, 2023. paper
Aubret, A., Matignon, L., Hassas, S.: DisTop: Discovering a Topological representation to learn diverse and rewarding skills. IEEE Transactions on Cognitive and Developmental Systems 2023. paper
Arthur Aubret, Laëtitia Matignon, Salima Hassas: ELSIM: End-to-End Learning of Reusable Skills Through Intrinsic Motivation. ECML/PKDD (2) 2020: 541-556. paper
Aubret, A., Matignon, L., Hassas, S.: ELSIM: End-to-end learning of reusable skills through intrinsic motivation. ICML 2020 Workshop LifelongML. paper
Aubret, A., Matignon, L., Hassas, S.: Étude de la motivation intrinsèque en apprentissage par renforcement. Journées Francophones Planification, Décision et Apprentissage, 2019. paper
Link thesis
Contact
Mail: ajp.aubret@gmail.com
Twitter: @Arthur_Aubret