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EyeCoD: Eye Tracking System Acceleration via FlatCam-Based Algorithm and Hardware Co-Design., , , , , , , , , and 4 other author(s). IEEE Micro, 43 (4): 88-97 (July 2023)NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation., , , , , and . CoRR, (2023)Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design., , , , , , and . ISCA, page 74:1-74:12. ACM, (2023)NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 10482-10493. PMLR, (2023)Max-Affine Spline Insights Into Deep Network Pruning., , , , , , , , and . Trans. Mach. Learn. Res., (2022)EyeCoD: eye tracking system acceleration via flatcam-based algorithm & accelerator co-design., , , , , , , , , and 4 other author(s). ISCA, page 610-622. ACM, (2022)Hint-Aug: Drawing Hints from Foundation Vision Transformers towards Boosted Few-shot Parameter-Efficient Tuning., , , , and . CVPR, page 11102-11112. IEEE, (2023)Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?, , , , and . ICLR, OpenReview.net, (2022)