Links
[email protected]
https://github.com/Jammf
https://orcid.org/0009-0005-6721-2344
https://www.linkedin.com/in/Jammf/
Hi there. This is an introduction.
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M. R. Kabir, J. Mochizuki-Freeman, and Z. Tiganj, "Deep reinforcement learning with time-scale invariant memory," accepted to The 39th Annual AAAI Conference on Artificial Intelligence, 2025, arXiv:2412.15292.
[pdf] J. Mochizuki-Freeman, M. R. Kabir, and Z. Tiganj, "Incorporating a cognitive model for evidence accumulation into deep reinforcement learning agents," in Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 46, 2024, pp. 5370-5376.
[pdf] J. Mochizuki-Freeman, S. S. Maini, and Z. Tiganj, "Characterizing neural activity in cognitively inspired RL agents during an evidence accumulation task," in 2023 International Joint Conference on Neural Networks (IJCNN), 2023, pp. 01-09, doi: 10.1109/ijcnn54540.2023.10191578.
[pdf] S. S. Maini, J. Mochizuki-Freeman, C. S. Indi, B. G. Jacques, P. B. Sederberg, M. W. Howard, and Z. Tiganj, "Representing Latent Dimensions Using Compressed Number Lines," in 2023 International Joint Conference on Neural Networks (IJCNN), 2023, pp. 1-10, doi: 10.1109/ijcnn54540.2023.10190998.
[pdf] J. Mochizuki-Freeman, M. R. Kabir, M. Gulecha, and Z. Tiganj. "Geometry of abstract learned knowledge in deep RL agents," in Proceedings of the 2nd NeurIPS Workshop on Symmetry and Geometry in Neural Representations, in Proceedings of Machine Learning Research, vol. 228, 2024, pp. 405-424.
[email protected]
https://github.com/Jammf
https://orcid.org/0009-0005-6721-2344
https://www.linkedin.com/in/Jammf/