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수츠케버 30 논문 연구
30개 논문을 한 편씩 읽으며 AI 구조·학습·검색·메모리의 병목을 깊게 소화하는 심화 목차.
101. The First Law of Complexodynamics▶202. The Unreasonable Effectiveness of RNNs▶303. Understanding LSTM Networks▶404. RNN Regularization▶505. Keeping Neural Networks Simple▶606. Pointer Networks▶707. ImageNet/AlexNet▶808. Order Matters: Seq2Seq for Sets▶909. GPipe▶1010. Deep Residual Learning (ResNet)▶1111. Dilated Convolutions▶1212. Neural Message Passing (GNNs)▶1313. Attention Is All You Need▶1414. Neural Machine Translation▶1515. Identity Mappings in ResNet▶1616. Relational Reasoning▶1717. Variational Lossy Autoencoder▶1818. Relational RNNs▶1919. The Coffee Automaton▶2020. Neural Turing Machines▶2121. Deep Speech 2 (CTC)▶2222. Scaling Laws▶2323. MDL Principle▶2424. Machine Super Intelligence▶2525. Kolmogorov Complexity▶2626. CS231n: CNNs for Visual Recognition▶2727. Multi-token Prediction▶2828. Dense Passage Retrieval▶2929. Retrieval-Augmented Generation▶3030. Lost in the Middle▶