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Emergence of Invariance and Disentanglement in Deep Representations

, and . (2017)cite arxiv:1706.01350Comment: Deep learning, neural network, representation, flat minima, information bottleneck, overfitting, generalization, sufficiency, minimality, sensitivity, information complexity, stochastic gradient descent, regularization, total correlation, PAC-Bayes.

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Mixed Differential Privacy in Computer Vision., , , , , and . CVPR, page 8366-8376. IEEE, (2022)Predicting Training Time Without Training., , , , and . NeurIPS, (2020)Towards Differential Relational Privacy and its use in Question Answering., , , , , , , , and . CoRR, (2022)Critical Learning Periods Emerge Even in Deep Linear Networks., , and . ICLR, OpenReview.net, (2024)Critical Learning Periods in Deep Networks., , and . ICLR (Poster), OpenReview.net, (2019)Task2Vec: Task Embedding for Meta-Learning., , , , , , , and . ICCV, page 6429-6438. IEEE, (2019)Mixed-Privacy Forgetting in Deep Networks., , , , and . CVPR, page 792-801. Computer Vision Foundation / IEEE, (2021)Task Adaptive Parameter Sharing for Multi-Task Learning., , , , , , and . CVPR, page 7551-7560. IEEE, (2022)Train/Test-Time Adaptation with Retrieval., , , , , and . CVPR, page 15911-15921. IEEE, (2023)LQF: Linear Quadratic Fine-Tuning., , , , and . CVPR, page 15729-15739. Computer Vision Foundation / IEEE, (2021)