| [1] |
A. Fawzi, M. Balog, A. Huang, T. Hubert, B. Romera-Paredes, M. Barekatain, et al. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature, 610 ( 2022), pp. 47-52. DOI: 10.1038/s41586-022-05172-4
|
| [2] |
D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 529 (7587) ( 2016), pp. 484-489. DOI: 10.1038/nature16961
|
| [3] |
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596 (7873) ( 2021), pp. 583-589. DOI: 10.1038/s41586-021-03819-2
|
| [4] |
S. O’Neill. Artificial intelligence cracks a 50-year-old grand challenge in biology. Engineering, 7 (6) (2021), pp. 706-708
|
| [5] |
S. O’Neill. Machine learning turbocharges structural biology. Engineering, 12 (2022), pp. 9-11
|
| [6] |
V. Strassen. Gaussian elimination is not optimal. Numer Math, 13 (4) (1969), pp. 354-356
|
| [7] |
Duan R, Wu H, Zhou R. Faster matrix multiplication via asymmetric hashing. 2023. arXiv:2210.10173v4.
|
| [8] |
S. Arora, B. Barak. Computational complexity:a modern approach, Cambridge University Press, Cambridge (2009), p. 286
|
| [9] |
J. Palmer. More super supercomputers. Engineering, 5 (3) (2019), pp. 357-358
|
| [10] |
Kauers M, Moosbauer J. Flip graphs for matrix multiplication; 2022. arXiv:2212.01175v1.
|
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