The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input.
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CoRR, January, 2025
Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization.
Neural Networks, April, 2023
Understanding the Brain using Machine Learning and Enhancing Machine Learning with Neuroscience
PhD thesis, 2023
Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus.
Cogn. Comput., 2023
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability.
CoRR, 2022
Adapting to Environment Changes Through Neuromodulation of Reinforcement Learning.
Proceedings of the From Animals to Animats 16, 2022
CARLsim 6: An Open Source Library for Large-Scale, Biologically Detailed Spiking Neural Network Simulation.
Proceedings of the International Joint Conference on Neural Networks, 2022
Uncertainty Aware Model Integration on Reinforcement Learning.
Proceedings of the International Joint Conference on Neural Networks, 2022
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
Neurorobots as a Means Toward Neuroethology and Explainable AI.
Frontiers Neurorobotics, 2020
Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
Spike trains encoding and threshold rescaling method for deep spiking neural networks.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017