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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 ComplexodynamicsNEW▶202. The Unreasonable Effectiveness of RNNsNEW▶303. Understanding LSTM NetworksNEW▶404. RNN RegularizationNEW▶505. Keeping Neural Networks SimpleNEW▶606. Pointer NetworksNEW▶707. ImageNet/AlexNetNEW▶808. Order Matters: Seq2Seq for SetsNEW▶909. GPipeNEW▶1010. Deep Residual Learning (ResNet)NEW▶1111. Dilated ConvolutionsNEW▶1212. Neural Message Passing (GNNs)NEW▶1313. Attention Is All You NeedNEW▶1414. Neural Machine TranslationNEW▶1515. Identity Mappings in ResNetNEW▶1616. Relational ReasoningNEW▶1717. Variational Lossy AutoencoderNEW▶1818. Relational RNNsNEW▶1919. The Coffee AutomatonNEW▶2020. Neural Turing MachinesNEW▶2121. Deep Speech 2 (CTC)NEW▶2222. Scaling LawsNEW▶2323. MDL PrincipleNEW▶2424. Machine Super IntelligenceNEW▶2525. Kolmogorov ComplexityNEW▶2626. CS231n: CNNs for Visual RecognitionNEW▶2727. Multi-token PredictionNEW▶2828. Dense Passage RetrievalNEW▶2929. Retrieval-Augmented GenerationNEW▶3030. Lost in the MiddleNEW▶