대표 읽을거리 지도

각 챕터의 개념이 어디서 등장하고 발전했는지 확인하기 위한 대표 자료다. 모든 문헌을 망라하지 않으며, 관심 주제를 찾은 뒤 원문으로 이동하기 위한 출발점으로 사용한다. 연도는 원칙적으로 학회 또는 저널 발표연도를 사용하며, 링크가 arXiv preprint를 가리켜도 같은 기준을 유지한다.

공통 학습 원리

• Goodfellow, Bengio & Courville (2016), Deep Learning
• Rosenblatt (1958), The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
• Rumelhart, Hinton & Williams (1986), Learning Representations by Back-Propagating Errors
• Glorot & Bengio (2010), Understanding the Difficulty of Training Deep Feedforward Neural Networks
• Kingma & Ba (2015), Adam: A Method for Stochastic Optimization
• Loshchilov & Hutter (2019), Decoupled Weight Decay Regularization
• He et al. (2016), Deep Residual Learning for Image Recognition

표현·CNN·NLP·Transformer·GNN

• Pearson (1901), On Lines and Planes of Closest Fit to Systems of Points in Space
• Fisher (1936), The Use of Multiple Measurements in Taxonomic Problems
• van der Maaten & Hinton (2008), Visualizing Data Using t-SNE
• McInnes, Healy & Melville (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• LeCun et al. (1998), Gradient-Based Learning Applied to Document Recognition
• Krizhevsky, Sutskever & Hinton (2012), ImageNet Classification with Deep Convolutional Neural Networks
• Mikolov et al. (2013), Efficient Estimation of Word Representations in Vector Space
• Sutskever, Vinyals & Le (2014), Sequence to Sequence Learning with Neural Networks
• Bahdanau, Cho & Bengio (2015), Neural Machine Translation by Jointly Learning to Align and Translate
• Vaswani et al. (2017), Attention Is All You Need
• Devlin et al. (2019), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Kipf & Welling (2017), Semi-Supervised Classification with Graph Convolutional Networks
• Veličković et al. (2018), Graph Attention Networks
• Gilmer et al. (2017), Neural Message Passing for Quantum Chemistry

생성모델

• Kingma & Welling (2014), Auto-Encoding Variational Bayes
• Goodfellow et al. (2014), Generative Adversarial Nets, 생성모델 전체 지도상의 비교 계열
• Ho, Jain & Abbeel (2020), Denoising Diffusion Probabilistic Models
• Song et al. (2021), Score-Based Generative Modeling through Stochastic Differential Equations
• Lipman et al. (2023), Flow Matching for Generative Modeling

Reinforcement Learning

• Watkins & Dayan (1992), Q-learning
• Williams (1992), Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
• Mnih et al. (2015), Human-level Control through Deep Reinforcement Learning
• Schulman et al. (2017), Proximal Policy Optimization Algorithms

AI4Science

• Karniadakis et al. (2021), Physics-informed machine learning
• Raissi, Perdikaris & Karniadakis (2019), Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
• Lu et al. (2021), Learning Nonlinear Operators via DeepONet Based on the Universal Approximation Theorem of Operators
• Li et al. (2021), Fourier Neural Operator for Parametric Partial Differential Equations
• Behler & Parrinello (2007), Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
• Schütt et al. (2017), SchNet: A Continuous-filter Convolutional Neural Network for Modeling Quantum Interactions
• Batzner et al. (2022), E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Batatia et al. (2022), MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

사용 원칙

• 소개 페이지에서는 원전의 문제 설정과 핵심 그림을 우선한다.
• 논문의 결과를 현재의 일반적 사실처럼 확장하지 않는다.
• 후속 연구로 바뀐 평가 방식이나 알려진 제한은 필요한 챕터에서 별도로 확인한다.
• 논문 PDF와 그림을 저장소에 그대로 복제하지 않고 공식 링크와 정확한 출처를 사용한다.