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Automated Multi-Stage Compression of Neural Networks., , , , , , and . ICCV Workshops, page 2501-2508. IEEE, (2019)Memory-Efficient Backpropagation through Large Linear Layers., , , , , and . CoRR, (2022)Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction., , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 26363-26381. PMLR, (2023)Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 42018-42045. PMLR, (2023)Optimal Control and Sensitivity Analysis for Two Risk Models., and . Communications in Statistics - Simulation and Computation, 45 (5): 1451-1466 (2016)Interpolated Adjoint Method for Neural ODEs., , , , , and . CoRR, (2020)Stable Low-Rank Tensor Decomposition for Compression of Convolutional Neural Network., , , , , , , , and . ECCV (29), volume 12374 of Lecture Notes in Computer Science, page 522-539. Springer, (2020)One time is not enough: iterative tensor decomposition for neural network compression., , , , , and . CoRR, (2019)Survey on Efficient Training of Large Neural Networks., , , , , , , , , and 1 other author(s). IJCAI, page 5494-5501. ijcai.org, (2022)Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs., , , , , and . NeurIPS, (2020)