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Suggestion: Cite the following papers #304
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@FinAminToastCrunch The link you shared is not working. Could you update the link so I can take at which part you are referring to here. |
Thanks Fin! Do you think you could issue a PR so that I could review it and
merge it in? I will edit it if needed after you issue a PR.
…On Thu, Jul 04, 2024 at 12:32 PM, Fin Amin ***@***.***> wrote:
Fixed:
https://harvard-edge.github.io/cs249r_book/contents/optimizations/
optimizations.html#legal-and-ethical-considerations
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And just as FYI. I typically avoid incorporating overly cutting-edge
material into the book. The criterion for inclusion is that the industry
must be actively adopting and utilizing the methods discussed. This
approach ensures the content remains relevant and avoids delving into an
excessive number of rapidly evolving topics. Does that make sense?
On Thu, Jul 04, 2024 at 12:44 PM, Janapa Reddi, Vijay ***@***.***>
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… Thanks Fin! Do you think you could issue a PR so that I could review it
and merge it in? I will edit it if needed after you issue a PR.
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On Thu, Jul 04, 2024 at 12:32 PM, Fin Amin ***@***.***>
wrote:
Fixed:
https://harvard-edge.github.io/cs249r_book/contents/optimizations/
optimizations.html#legal-and-ethical-considerations
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Done! And yes, makes sense. |
Dealt with in issue #306 and PR |
Thanks @FinAminToastCrunch I finally got a chance to review the changes and make some tweaks an close it. |
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In reference to Ethical Considerations on Pruning, I would suggest citing:
Tran, Cuong, et al. "Pruning has a disparate impact on model accuracy." Advances in neural information processing systems 35 (2022): 17652-17664.
As it discusses how pruning can harm accuracy with respect to inferencing on certain ethnicities.
In reference to Establishing Criteria for Pruning, I believe these two papers would be relevant to cite for the portions on importance scores as they tie together more recent work concerning gradient flow and neuron magnitude:
J. Rachwan, D. Z ̈ugner, B. Charpentier, S. Geisler, M. Ayle, and
S. G ̈unnemann, “Winning the lottery ahead of time: Efficient early
network pruning,” in International Conference on Machine Learning,
ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (K. Chaudhuri,
S. Jegelka, L. Song, C. Szepesv ́ari, G. Niu, and S. Sabato, eds.),
vol. 162 of Proceedings of Machine Learning Research, pp. 18293–
18309, PMLR, 2022.
E. S. Lubana and R. P. Dick, “A gradient flow framework for analyzing
network pruning,” CoRR, vol. abs/2009.11839, 2020.
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