Shunyao Zhang

This page is a disambiguation page, it actually contains multiple papers from persons of the same or a similar name.

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Bibliography

2026
Knowledge-driven MOEA/D for optimizing the road network of open-pit mines.
Expert Syst. Appl., 2026

Online spatial-temporal prediction for dynamic constrained multiobjective evolutionary optimization.
Expert Syst. Appl., 2026

2025
Compressive Sensing Photoacoustic Imaging Receiver With Matrix-Vector-Multiplication SAR ADC.
IEEE J. Solid State Circuits, November, 2025

35.2 A Spatial-Domain Compressive-Sensing Photoacoustic Imager with Matrix-Multiplying SAR ADC.
Proceedings of the IEEE International Solid-State Circuits Conference, 2025

NEWER: Neural Estimation of Wavelet-Embedded Representations.
Proceedings of the 59th Asilomar Conference on Signals, 2025

2023
NetDistiller: Empowering Tiny Deep Learning via In Situ Distillation.
IEEE Micro, 2023

EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design.
IEEE Micro, 2023

Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design.
Proceedings of the 50th Annual International Symposium on Computer Architecture, 2023

NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations.
Proceedings of the International Conference on Machine Learning, 2023

Hint-Aug: Drawing Hints from Foundation Vision Transformers towards Boosted Few-shot Parameter-Efficient Tuning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Max-Affine Spline Insights Into Deep Network Pruning.
Trans. Mach. Learn. Res., 2022

EyeCoD: eye tracking system acceleration via flatcam-based algorithm & accelerator co-design.
Proceedings of the ISCA '22: The 49th Annual International Symposium on Computer Architecture, New York, New York, USA, June 18, 2022

Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Proceedings of the Tenth International Conference on Learning Representations, 2022


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