개요

Hugging Face Seoul Meetup 정리에 참고한 자료를 모았습니다.

HuggingFace Seoul Meetup Reference

• Hugging Face Seoul Meetup
• HAE-RAE
• HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
• KMMLU: Measuring Massive Multitask Language Understanding in Korean
• HRET: A Self-Evolving LLM Evaluation Toolkit for Korean
• HAE-RAE Evaluation Toolkit
• What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models
• SOOHAK: A Mathematician-Curated Benchmark for Evaluating Research-level Math Capabilities of LLMs
• K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
• Kiwi: 통계적 언어 모델과 Skip-Bigram을 이용한 한국어 형태소 분석기 구현
• Kiwi
• Kiwi Farm
• SwTokenizer API Documentation
• Morpheme Matters: Morpheme-Based Subword Tokenization for Korean Language Models
• Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean
• Bllossom
• KORMo: Korean Open Reasoning Model for Everyone
• KORMo Team
• KORMo Tutorial
• KORMo-VL
• Scaling Laws for Neural Language Models
• Training Compute-Optimal Large Language Models
• ReAct: Synergizing Reasoning and Acting in Language Models
• Executable Code Actions Elicit Better LLM Agents
• smolagents
• smolagents Documentation
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• Demystifying Evals for AI Agents
• τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
• SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
• Solar Open Technical Report
• Solar Open 100B
• OpenRouter AI Model Rankings
• Exclusive Q&A: John Carmack’s “Different Path” to Artificial General Intelligence
• Ilya Sutskever Reading List

Ilya Sutskever 27개 읽기 목록

1. Keeping Neural Networks Simple — Geoffrey Hinton, Drew van Camp
2. A Tutorial Introduction to the Minimum Description Length Principle — Peter Grünwald
3. Kolmogorov Complexity and Algorithmic Randomness — Alexander Shen, Vladimir Uspensky, Nikolay Vereshchagin
4. ImageNet Classification with Deep Convolutional Neural Networks — Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton
5. Deep Residual Learning for Image Recognition — Kaiming He et al.
6. Identity Mappings in Deep Residual Networks — Kaiming He et al.
7. Multi-Scale Context Aggregation by Dilated Convolutions — Fisher Yu, Vladlen Koltun
8. The Unreasonable Effectiveness of Recurrent Neural Networks — Andrej Karpathy
9. Understanding LSTM Networks — Christopher Olah
10. Recurrent Neural Network Regularization — Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals
11. Order Matters: Sequence to Sequence for Sets — Oriol Vinyals et al.
12. Pointer Networks — Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
13. Neural Machine Translation by Jointly Learning to Align and Translate — Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
14. Attention Is All You Need — Ashish Vaswani et al.
15. The Annotated Transformer — Alexander Rush
16. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism — Yanping Huang et al.
17. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin — Dario Amodei et al.
18. Scaling Laws for Neural Language Models — Jared Kaplan et al.
19. Neural Turing Machines — Alex Graves, Greg Wayne, Ivo Danihelka
20. A Simple Neural Network Module for Relational Reasoning — Adam Santoro et al.
21. Relational Recurrent Neural Networks — Adam Santoro et al.
22. Variational Lossy Autoencoder — Xi Chen et al.
23. Neural Message Passing for Quantum Chemistry — Justin Gilmer et al.
24. The First Law of Complexodynamics — Scott Aaronson
25. Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton — Scott Aaronson, Sean M. Carroll, Lauren Ouellette
26. Machine Super Intelligence — Shane Legg
27. CS231n: Convolutional Neural Networks for Visual Recognition — Stanford