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Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions.

, , , , , , , and . CPAIOR, volume 13884 of Lecture Notes in Computer Science, page 200-218. Springer, (2023)

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Conditional Expectation Propagation., and . UAI, volume 115 of Proceedings of Machine Learning Research, page 28-37. AUAI Press, (2019)Diffusion-Generative Multi-Fidelity Learning for Physical Simulation., , , and . CoRR, (2023)Physics Informed Deep Kernel Learning., , , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 1206-1218. PMLR, (2022)AutoIP: A United Framework to Integrate Physics into Gaussian Processes., , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 14210-14222. PMLR, (2022)Complexity-Aware Deep Symbolic Regression with Robust Risk-Seeking Policy Gradients., , , and . CoRR, (2024)Annealed Sparsity via Adaptive and Dynamic Shrinking., , , , , , , , and . KDD, page 1325-1334. ACM, (2016)Asynchronous Distributed Variational Gaussian Process for Regression., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 2788-2797. PMLR, (2017)Probabilistic Streaming Tensor Decomposition., , , and . ICDM, page 99-108. IEEE Computer Society, (2018)Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models., , , , and . IJCAI, page 1867-1973. IJCAI/AAAI Press, (2016)Streaming Factor Trajectory Learning for Temporal Tensor Decomposition., , , , , and . NeurIPS, (2023)