Spike trains encoding and threshold rescaling method for deep spiking neural networks. - Yang Xu; Huajin Tang; Jinwei Xing; Hongying Li (SSCI, 2017)

CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters. - Ting-Shuo Chou; Hirak Jyoti Kashyap; Jinwei Xing; Stanislav Listopad; Emily L. Rounds; Michael Beyeler; Nikil D. Dutt; Jeffrey L. Krichmar (IJCNN, 2018)

Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation. - Jinwei Xing; Xinyun Zou; Jeffrey L. Krichmar (IJCNN, 2020)

Neurorobots as a Means Toward Neuroethology and Explainable AI. - Kexin Chen 002; Tiffany Hwu; Hirak Jyoti Kashyap; Jeffrey L. Krichmar; Kenneth Michael Stewart; Jinwei Xing; Xinyun Zou (Frontiers Neurorobotics, 2020)

Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. - Jinwei Xing; Takashi Nagata; Kexin Chen 002; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (AAAI, 2021)

Uncertainty Aware Model Integration on Reinforcement Learning. - Takashi Nagata; Jinwei Xing; Tsutomu Kumazawa; Emre Neftci (IJCNN, 2022)

CARLsim 6: An Open Source Library for Large-Scale, Biologically Detailed Spiking Neural Network Simulation. - Lars Niedermeier; Kexin Chen 002; Jinwei Xing; Anup Das 001; Jeffrey Kopsick; Eric Scott; Nate Sutton; Killian Weber; Nikil D. Dutt; Jeffrey L. Krichmar (IJCNN, 2022)

Adapting to Environment Changes Through Neuromodulation of Reinforcement Learning. - Jinwei Xing; Xinyun Zou; Praveen K. Pilly; Nicholas A. Ketz; Jeffrey L. Krichmar (SAB, 2022)

Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability. - Jinwei Xing; Takashi Nagata; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (CoRR, 2022)

Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. - Jeffrey D. Kopsick; Carolina Tecuatl; Keivan Moradi; Sarojini M. Attili; Hirak J. Kashyap; Jinwei Xing; Kexin Chen 002; Jeffrey L. Krichmar; Giorgio A. Ascoli (Cogn. Comput., 2023)

Understanding the Brain using Machine Learning and Enhancing Machine Learning with Neuroscience - Jinwei Xing (2023)

Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization. - Jinwei Xing; Takashi Nagata; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (Neural Networks, 2023)

The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input. - Alon Jacovi; Andrew Wang; Chris Alberti; Connie Tao; Jon Lipovetz; Kate Olszewska; Lukas Haas; Michelle Liu; Nate Keating; Adam Bloniarz; Carl Saroufim; Corey Fry; et al. (CoRR, 2025)