松尾研 LLM コミュニティ "Paper & Hacks Vol.20"
https://matsuolab-community.connpass.com/event/332695/

スケーリング法則が何故スケーラブルなのか?
余振軒立教大学人工知能科学研究科

Reference 

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Elliot Paquette+. 4+3 Phases of Compute-Optimal Neural Scaling Laws. 2024. In arXiv:2405.15074v1  https://arxiv.org/abs/2405.15074v1 
https://arxiv.org/pdf/2405.15074v1

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Tatsunori Hashimoto+, CS336: Language Modeling from Scratch, Lecture 9, 
https://stanford-cs336.github.io/spring2024/
 

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Albert Gu+. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. 2024. In arXiv:2312.00752v2   https://arxiv.org/abs/2312.00752v2 
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[106] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. “Attention Is All You Need”. In: Advances in Neural Information Processing Systems (NeurIPS). 2017.
[107] Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, and Chris Pal. “On Orthogonality and Learning Recurrent Networks with Long Term Dependencies”. In: International Conference on Machine Learning. PMLR. 2017, pp. 3570–3578.
[108] Jue Wang, Wentao Zhu, Pichao Wang, Xiang Yu, Linda Liu, Mohamed Omar, and Raffay Hamid. “Selective Structured State-Spaces for Long-form Video Understanding”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, pp. 6387–6397.
[109] Pete Warden. “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition”. In: ArXiv abs/1804.03209 (2018). https://arxiv.org/pdf/
[110] Samuel Williams, Andrew Waterman, and David Patterson. “Roofline: An Insightful Visual Performance Model for Multicore Architectures”. In: Communications of the ACM 52.4 (2009), pp. 65–76.
[111] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. “CondConv: Conditionally Parameterized Convolu-tions for Efficient Inference”. In: Advances in Neural Information Processing Systems (NeurIPS) 32 (2019).
[112] Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. “HellaSwag: Can a Machine Really Finish Your Sentence?” In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
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Lin Zheng, Chong Wang, and Lingpeng Kong. “Linear complexity randomized self-attention mechanism”. In: International Conference on Machine Learning. PMLR. 2022, pp. 27011–27041.
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Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, and Jianfeng Gao. “Efficient Long Sequence Modeling via State Space Augmented Transformer”. In: arXiv preprint arXiv:2212.08136 (2022).https://arxiv.org/pdf/

[4]

Samy Jelassi+. Repeat After Me: Transformers are Better than State Space Models at Copying. 2024. In arXiv:2402.01032   https://arxiv.org/abs/2402.01032 
 https://arxiv.org/pdf/2402.01032

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<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words and/or centences in order.

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https://qiita.com/kaizen_nagoya/items/ae391b77fffb098b8fb4

プログラマの「プログラムが書ける」思い込みは強みだ。３つの理由。仮説（168）統計と確率(17) , OSEK(79)
https://qiita.com/kaizen_nagoya/items/bc5dd86e414de402ec29

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

これからの情報伝達手段の在り方について考えてみよう。炎上と便乗。
https://qiita.com/kaizen_nagoya/items/71a09077ac195214f0db

ISO/IEC JTC1 SC7 Software and System Engineering
https://qiita.com/kaizen_nagoya/items/48b43f0f6976a078d907

アクセシビリティの知見を発信しよう！（再び）
https://qiita.com/kaizen_nagoya/items/03457eb9ee74105ee618

統計論及確率論輪講（再び）
https://qiita.com/kaizen_nagoya/items/590874ccfca988e85ea3

読者の心をグッと惹き寄せる7つの魔法
https://qiita.com/kaizen_nagoya/items/b1b5e89bd5c0a211d862

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

ソースコードで議論しよう。日本語で議論するの止めましょう（あるプログラミング技術の議論報告）
https://qiita.com/kaizen_nagoya/items/8b9811c80f3338c6c0b0

脳内コンパイラの3つの危険
https://qiita.com/kaizen_nagoya/items/7025cf2d7bd9f276e382

心理学の本を読むよりはコンパイラ書いた方がよくね。仮説（34）
https://qiita.com/kaizen_nagoya/items/fa715732cc148e48880e

NASAを超えるつもりがあれば読んでください。
https://qiita.com/kaizen_nagoya/items/e81669f9cb53109157f6

データサイエンティストの気づき!「勉強して仕事に役立てない人。大嫌い!!」『それ自分かも?』ってなった!!!
https://qiita.com/kaizen_nagoya/items/d85830d58d8dd7f71d07

「ぼくの好きな先生」「人がやらないことをやれ」プログラマになるまで。仮説（37）　
https://qiita.com/kaizen_nagoya/items/53e4bded9fe5f724b3c4

なぜ経済学徒を辞め、計算機屋になったか（経済学部入学前・入学後・卒業後対応）転職(1)
https://qiita.com/kaizen_nagoya/items/06335a1d24c099733f64

プログラミング言語教育のXYZ。仮説（52）
https://qiita.com/kaizen_nagoya/items/1950c5810fb5c0b07be4

【24卒向け】9ヶ月後に年収１０００万円を目指す。二つの関門と三つの道。
https://qiita.com/kaizen_nagoya/items/fb5bff147193f726ad25

「【25卒向け】Qiita Career Meetup for STUDENT」予習の勧め
https://qiita.com/kaizen_nagoya/items/00eadb8a6e738cb6336f

大学入試不合格でも筆記試験のない大学に入って卒業できる。卒業しなくても博士になれる。
https://qiita.com/kaizen_nagoya/items/74adec99f396d64b5fd5

全世界の不登校の子供たち「博士論文」を書こう。世界子供博士論文遠隔実践中心安全(99)
https://qiita.com/kaizen_nagoya/items/912d69032c012bcc84f2

小川メソッド　覚え（書きかけ）
https://qiita.com/kaizen_nagoya/items/3593d72eca551742df68

DoCAP（ドゥーキャップ）って何ですか？
https://qiita.com/kaizen_nagoya/items/47e0e6509ab792c43327

views 20,000越え自己記事一覧
https://qiita.com/kaizen_nagoya/items/58e8bd6450957cdecd81

Views１万越え、もうすぐ１万記事一覧最近いいねをいただいた213記事
https://qiita.com/kaizen_nagoya/items/d2b805717a92459ce853

amazon 殿堂入りNo1レビュアになるまで。仮説(102)
https://qiita.com/kaizen_nagoya/items/83259d18921ce75a91f4

100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.

文書履歴(document history)

ver. 0.01 初稿　20241022

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