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Optimal Statistical and Computational Rates for One Bit Matrix Completion., and . AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 426-434. JMLR.org, (2016)Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent., , , , , , and . ICLR, OpenReview.net, (2023)Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization., , , , , and . CoRR, (2017)GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training., , , , , and . NeurIPS, page 16410-16422. (2021)Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks., , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 3607-3616. PMLR, (2020)Witchcraft: Efficient PGD Attacks with Random Step Size., , , , , , and . ICASSP, page 3747-3751. IEEE, (2020)Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization., , and . BMVC, BMVA Press, (2017)FedAQT: Accurate Quantized Training with Federated Learning., , , , , , , , and . ICASSP, page 6100-6104. IEEE, (2024)Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers., , , , , and . CoRR, (2020)Certified Defenses for Adversarial Patches., , , , , and . ICLR, OpenReview.net, (2020)