Altering embryonic cardiac dynamics with optical pacing. - Lindsy M. Peterson; Matt McPheeters; Lee Barwick; Shi Gu; Andrew M. Rollins; Michael W. Jenkins (EMBC, 2012)

Optical mapping of optically paced embryonic hearts. - Yves T. Wang; Shi Gu; Andrew M. Rollins; Michael W. Jenkins (EMBC, 2013)

Controllability of Brain Networks. - Shi Gu; Fabio Pasqualetti; Matthew Cieslak; Scott T. Grafton; Danielle S. Bassett (CoRR, 2014)

Stimulation-Based Control of Dynamic Brain Networks. - Sarah Feldt Muldoon; Fabio Pasqualetti; Shi Gu; Matthew Cieslak; Scott T. Grafton; Jean M. Vettel; Danielle S. Bassett (PLoS Comput. Biol., 2016)

On Structural Controllability of Symmetric (Brain) Networks. - Tommaso Menara; Shi Gu; Danielle S. Bassett; Fabio Pasqualetti (CoRR, 2017)

The energy landscape underpinning module dynamics in the human brain connectome. - Arian Ashourvan; Shi Gu; Marcelo Gomes Mattar; Jean M. Vettel; Danielle S. Bassett (NeuroImage, 2017)

Optimal trajectories of brain state transitions. - Shi Gu; Richard F. Betzel; Marcelo Gomes Mattar; Matthew Cieslak; Philip R. Delio; Scott T. Grafton; Fabio Pasqualetti; Danielle S. Bassett (NeuroImage, 2017)

Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder. - Huzheng Yang; Xiaoxiao Li; Yifan Wu; Siyi Li; Su Lu; James S. Duncan; James C. Gee; Shi Gu (MICCAI (3), 2019)

RE: Warnings and caveats in brain controllability. - Fabio Pasqualetti; Shi Gu; Danielle S. Bassett (NeuroImage, 2019)

Robust Medical Image Segmentation from Non-expert Annotations with Tri-network. - Tianwei Zhang; Lequan Yu; Na Hu; Su Lv; Shi Gu (MICCAI (4), 2020)

Learning in School: Multi-teacher Knowledge Inversion for Data-Free Quantization. - Yuhang Li 001; Feng Zhu 006; Ruihao Gong; Mingzhu Shen; Fengwei Yu; Shaoqing Lu; Shi Gu (CoRR, 2020)

Benchmarking Measures of Network Controllability on Canonical Graph Models. - Elena Wu-Yan; Richard F. Betzel; Evelyn Tang; Shi Gu; Fabio Pasqualetti; Danielle S. Bassett (J. Nonlinear Sci., 2020)

Empirical study of correlations in the fitness landscapes of combinatorial optimization problems. - Longfei Zhang; Ke Li 001; Shi Gu (GECCO Companion, 2021)

MixMix: All You Need for Data-Free Compression Are Feature and Data Mixing. - Yuhang Li 001; Feng Zhu 006; Ruihao Gong; Mingzhu Shen; Xin Dong 009; Fengwei Yu; Shaoqing Lu; Shi Gu (ICCV, 2021)

Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks. - Shikuang Deng; Shi Gu (ICLR, 2021)

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction. - Yuhang Li 001; Ruihao Gong; Xu Tan; Yang Yang; Peng Hu; Qi Zhang; Fengwei Yu; Wei Wang 059; Shi Gu (ICLR, 2021)

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Ruihao Gong; Shi Gu (ICML, 2021)

Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks. - Yuhang Li 001; Yufei Guo; Shanghang Zhang; Shikuang Deng; Yongqing Hai; Shi Gu (NeurIPS, 2021)

Multi-modal Attention Network for Stock Movements Prediction. - Shwai He; Shi Gu (CoRR, 2021)

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations. - Zhen Yang; Qawi K. Telesford; Alexandre R. Franco; Ryan Lim; Shi Gu; Ting Xu 001; Lei Ai; Francisco X. Castellanos; Chao-Gan Yan; Stanley J. Colcombe; Michael P. Milham (NeuroImage, 2021)

A Unified Framework for Generalized Low-Shot Medical Image Segmentation With Scarce Data. - Hengji Cui; Dong Wei 004; Kai Ma 002; Shi Gu; Yefeng Zheng 001 (IEEE Trans. Medical Imaging, 2021)

Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. - Shikuang Deng; Yuhang Li 001; Shanghang Zhang; Shi Gu (ICLR, 2022)

When Sparsity Meets Dynamic Convolution. - Shwai He; Yuhang Li 001; Chenbo Jiang; Shi Gu (CoRR, 2022)

Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Shi Gu (CoRR, 2022)

Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks. - Shikuang Deng; Hao Lin; Yuhang Li 001; Shi Gu (ICML, 2023)

Co-learning Semantic-Aware Unsupervised Segmentation for Pathological Image Registration. - Yang Liu; Shi Gu (MICCAI (10), 2023)

Train Once, Deploy Anywhere: Edge-Guided Single-source Domain Generalization for Medical Image Segmentation. - Jun Jiang; Shi Gu (MIDL, 2024)

Spiking Token Mixer: An event-driven friendly Former structure for spiking neural networks. - Shikuang Deng; Yuhang Wu; Kangrui Du; Shi Gu (NeurIPS, 2024)

A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data. - Yifan Wu; Yang Liu; Yue Yang; Michael S. Yao; Wenli Yang; Xuehui Shi; Lihong Yang; Dongjun Li; Yueming Liu; James C. Gee; Xuan Yang; Wenbin Wei 007; et al. (CoRR, 2024)

Self-supervised learning for medical image data with anatomy-oriented imaging planes. - Tianwei Zhang; Dong Wei 004; Mengmeng Zhua; Shi Gu; Yefeng Zheng 001 (Medical Image Anal., 2024)

Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration. - Yuhang Li 001; Shikuang Deng; Xin Dong 009; Shi Gu (Int. J. Comput. Vis., 2024)

Hybrid Concept Bottleneck Models. - Yang Liu; Tianwei Zhang; Shi Gu (CVPR, 2025)

Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness. - Kangrui Du; Yuhang Wu; Shikuang Deng; Shi Gu (ICLR, 2025)

Enhancing source-free domain adaptation in Medical Image Segmentation via regulated model self-training. - Tianwei Zhang; Kang Li 007; Shi Gu; Pheng-Ann Heng (Medical Image Anal., 2025)