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PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices., , and . CoRR, (2022)Scalable and Practical Natural Gradient for Large-Scale Deep Learning., , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (1): 404-415 (2022)Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation., , , and . HPCS, page 186-192. IEEE, (2017)Rich Information is Affordable: A Systematic Performance Analysis of Second-order Optimization Using K-FAC., , , , and . KDD, page 2145-2153. ACM, (2020)Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias., , , and . CoRR, (2022)Neural Graph Databases., , , , , , , and . LoG, volume 198 of Proceedings of Machine Learning Research, page 31. PMLR, (2022)Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks., and . ICANN (2), volume 10614 of Lecture Notes in Computer Science, page 459-466. Springer, (2017)Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs., , , , , and . CoRR, (2018)Efficient Quantized Sparse Matrix Operations on Tensor Cores., , and . SC, page 37:1-37:15. IEEE, (2022)Performance Optimizations and Analysis of Distributed Deep Learning with Approximated Second-Order Optimization Method., , , , , and . ICPP Workshops, page 21:1-21:8. ACM, (2019)