Reference
논문 레퍼런스 허브. 원문 PDF → sources/ 요약 → wiki/ 상세 페이지.
논문이 아닌 웹 아티클·에세이는 → Articles
Papers (연도순)
| # | 연도 | 저자 | 제목 | 분야 | Source | Wiki |
|---|---|---|---|---|---|---|
| 1 | 1854 | Boole | An Investigation of the Laws of Thought | foundations | src | wiki |
| 2 | 1936 | Turing | On Computable Numbers | foundations | src | wiki |
| 3 | 1938 | Shannon | Symbolic Analysis of Relay and Switching Circuits | foundations | src | wiki |
| 4 | 1943 | McCulloch, Pitts | A Logical Calculus of Nervous Activity | foundations | src | wiki |
| 5 | 1945 | von Neumann | First Draft of a Report on the EDVAC | foundations | src | wiki |
| 6 | 1948 | Shannon | A Mathematical Theory of Communication | foundations | src | wiki |
| 7 | 1948 | Wiener | Cybernetics: Or Control and Communication in the Animal and the Machine | foundations | src | wiki |
| 8 | 1949 | Shannon | Communication in the Presence of Noise | foundations | src | wiki |
| 9 | 1949 | Hebb | The Organization of Behavior | neuroscience | src | wiki |
| 10 | 1950 | Turing | Computing Machinery and Intelligence | foundations | src | wiki |
| 11 | 1951 | Shannon | Prediction and Entropy of Printed English | foundations | src | wiki |
| 12 | 1953 | Wittgenstein | Philosophical Investigations | foundations | src | wiki |
| 13 | 1955 | McCarthy et al. | A Proposal for the Dartmouth Summer Research Project | foundations | src | wiki |
| 14 | 1957 | Bellman | Dynamic Programming | reinforcement-learning | src | wiki |
| 15 | 1958 | Rosenblatt | The Perceptron | foundations | src | wiki |
| 16 | 1958 | McCarthy | Programs with Common Sense | foundations | src | wiki |
| 17 | 1958 | Simondon | Du mode d’existence des objets techniques | foundations | src | wiki |
| 18 | 1965 | Robinson | A Machine-Oriented Logic (Resolution Principle) | foundations | src | wiki |
| 19 | 1966 | von Neumann | Theory of Self-Reproducing Automata | artificial-life | src | wiki |
| 20 | 1969 | Minsky, Papert | Perceptrons | foundations | src | wiki |
| 21 | 1969 | McCarthy, Hayes | Some Philosophical Problems from the Standpoint of AI | foundations | src | wiki |
| 22 | 1972 | Bateson | Steps to an Ecology of Mind | foundations | src | wiki |
| 23 | 1974 | Minsky | A Framework for Representing Knowledge | foundations | src | wiki |
| 24 | 1974 | Werbos | Beyond Regression (Backpropagation) | foundations | src | wiki |
| 25 | 1975 | Holland | Adaptation in Natural and Artificial Systems | artificial-life | src | wiki |
| 26 | 1976 | Newell, Simon | Computer Science as Empirical Inquiry | foundations | src | wiki |
| 27 | 1982 | Hopfield | Neural Networks and Physical Systems with Emergent Collective Computational Abilities | foundations | src | wiki |
| 28 | 1986 | Rumelhart, Hinton, Williams | Learning by Back-propagating Errors | foundations | src | wiki |
| 29 | 1986 | White et al. | The Connectome of C. elegans | artificial-life | src | wiki |
| 30 | 1987 | Reynolds | Flocks, Herds, and Schools: A Distributed Behavioral Model | artificial-life | src | wiki |
| 31 | 1988 | Sutton | Learning to Predict by Temporal Differences | reinforcement-learning | src | wiki |
| 32 | 1990 | Langton | Computation at the Edge of Chaos | artificial-life | src | wiki |
| 33 | 1991 | Brooks | Intelligence Without Representation | artificial-life | src | wiki |
| 34 | 1992 | Ray | An Approach to the Synthesis of Life (Tierra) | artificial-life | src | wiki |
| 35 | 1992 | Watkins, Dayan | Q-Learning | reinforcement-learning | src | wiki |
| 36 | 1992 | Williams | REINFORCE | reinforcement-learning | src | wiki |
| 37 | 1993 | Schultz et al. | Responses of Monkey Dopamine Neurons | neuroscience | src | wiki |
| 38 | 1995 | Cortes, Vapnik | Support-Vector Networks | foundations | src | wiki |
| 39 | 1997 | Hochreiter, Schmidhuber | Long Short-Term Memory | foundations | src | wiki |
| 40 | 1997 | Schultz, Dayan, Montague | A Neural Substrate of Prediction and Reward | neuroscience | src | wiki |
| 41 | 1998 | LeCun et al. | Gradient-Based Learning Applied to Document Recognition (LeNet) | foundations | src | wiki |
| 42 | 2000 | Pearl | Causality: Models, Reasoning, and Inference | foundations | src | wiki |
| 43 | 2006 | Hinton, Osindero, Teh | A Fast Learning Algorithm for Deep Belief Nets | foundations | src | wiki |
| 44 | 2012 | Krizhevsky, Sutskever, Hinton | ImageNet Classification with Deep CNNs (AlexNet) | foundations | src | wiki |
| 45 | 2013 | Kingma, Welling | Auto-Encoding Variational Bayes (VAE) | transformers | src | wiki |
| 46 | 2014 | Goodfellow et al. | Generative Adversarial Nets | foundations | src | wiki |
| 47 | 2015 | Mnih et al. | Human-level Control through Deep RL (DQN) | reinforcement-learning | src | wiki |
| 48 | 2016 | Silver et al. | Mastering Go with Deep Neural Networks (AlphaGo) | reinforcement-learning | src | wiki |
| 49 | 2017 | Vaswani et al. | Attention Is All You Need | transformers | src | wiki |
| 50 | 2018 | Radford et al. | Improving Language Understanding by Generative Pre-Training (GPT-1) | transformers | src | wiki |
| 51 | 2019 | Devlin et al. | BERT | transformers | src | wiki |
| 52 | 2019 | Sutton | The Bitter Lesson | foundations | src | wiki |
| 53 | 2020 | Dosovitskiy et al. | An Image is Worth 16x16 Words (ViT) | transformers | src | wiki |
| 54 | 2020 | Ho, Jain, Abbeel | Denoising Diffusion Probabilistic Models (DDPM) | transformers | src | wiki |
| 55 | 2020 | Kaplan et al. | Scaling Laws for Neural Language Models | transformers | src | wiki |
| 56 | 2020 | Lewis et al. | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (RAG) | transformers | src | wiki |
| 57 | 2020 | Brown et al. | Language Models are Few-Shot Learners (GPT-3) | transformers | src | wiki |
| 58 | 2021 | Crawford | Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence | foundations | src | wiki |
| 59 | 2021 | Hu et al. | LoRA: Low-Rank Adaptation of Large Language Models | transformers | src | wiki |
| 60 | 2022 | Wei et al. | Chain-of-Thought Prompting | transformers | src | wiki |
| 61 | 2022 | LeCun | A Path Towards Autonomous Machine Intelligence | world-models | src | wiki |
| 62 | 2023 | Han et al. | Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey | transformers | src | wiki |
| 63 | 2023 | Winding et al. | The Connectome of an Insect Brain (Drosophila) | artificial-life | src | wiki |
| 64 | 2024 | Edge et al. | From Local to Global: GraphRAG | transformers | src | wiki |
| 65 | 2024 | Behrouz, Zhong, Mirrokni | Titans: Learning to Memorize at Test Time | transformers | src | wiki |
| 66 | 2025 | Silver, Sutton | Welcome to the Era of Experience | reinforcement-learning | src | wiki |
| 67 | 2025 | Kaushik et al. | The Universal Weight Subspace Hypothesis | transformers | src | wiki |
| 68 | 2025 | Behrouz et al. | Nested Learning: The Illusion of Deep Learning Architectures | transformers | src | wiki |
| 69 | 2026 | Maes et al. | LeWorldModel: Stable End-to-End JEPA | world-models | src | wiki |
| 70 | 2026 | Berg et al. | Sexual Dimorphism in the Complete Drosophila Male CNS Connectome | artificial-life | src | wiki |
Categories
- foundations — Computability, logic, foundational mathematics
- neuroscience — Dopamine, reinforcement learning, reward prediction
- artificial-life — Cellular automata, genetic algorithms, digital evolution
- reinforcement-learning — TD learning, Q-learning, policy gradients, DQN, AlphaGo
- transformers — Transformer architecture and variants
- world-models — Latent world models, JEPA, planning
- overviews — Synthesis pages integrating multiple papers
- Symbolism vs. Connectionism — Two theories of intelligence
- AI Chip and Memory — GPU/ASIC, HBM, memory hierarchy, CoWoS packaging
- AI Data Center — Building the AI factory: 1GW economics, power, supply chain