- 🐷 := fundamental theories
- 👨👩👧👦 := series of papers on a same topic
- 🐶 := a single paper

## 1 **Maths**

### 1.1 Linear Algebra & Functional Analysis

### 1.2 Probability & Statistics

### 1.3 Optimization & Numerical Computation

## 2 **ML**

### 2.1 Fundamentals

### 2.2 Deep Clustering & Subspace Clustering

- 🐶 Unsupervised deep embedding for clustering analysis. (ICML 2016, DEC)
- 🐶 Joint Unsupervised Learning of Deep Representations and Image Clusters (CVPR 2016, JULE)
- 👨👩👧👦 Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning (AAAI 2020, CGCN)
- including: VAE, VGAE, GMVAE, VaDE, DAEGC, SDCN, AGC, DGG, CGCN

- 👨👩👧👦 Sparse self representation subspace clustering
- including: SSC, SSSC, SSConvSCN

- 🐶 Low rank representation subspace clustering
- math fundamental: 🐷 low rank matrix factorization

### 2.3 Spectral Methods & GNN

- 🐷 Spectral Clustering
- 🐷 Spectra of Simple Graphs
- 🐷 Graph Fourier Transformation & convolution
- 🐶 Semi-Supervised Classification with Graph Convolutional Network (ICML 2017, GCN)
- 🐶 Beyond Low-frequency Information in Graph Convolutional Networks (AAAI 2021, FAGCN)

### 2.4 Few-shot Learning

#### 2.4.1 Metric Learning

- 🐶 Siamese neural networks for one-shot image recognition (ICML 2015, Siamese Net)
- 🐶 Matching networks for one shot learning (NeuralIPS 2016, Matching Net)
- 🐶 Prototypical Networks for Few-shot Learning (NeuralIPS 2017, Prototypical Net)
- 🐶 Learning to Compare: Relation Network for Few-Shot Learning (CVPR 2018, Relation Net)

#### 2.4.2 Meta Optimizer Based

- 🐶 Optimization as a Model for Few-Shot Learning (ICLR 2017, meta-LSTM)
- 🐶 Model-agnostic meta-learning for fast adaptation of deep networks (ICML 2017, MAML)

#### 2.4.3 Improvements

- 👨👩👧👦 Task-specific Few-shot learning

## 3 **NLP**

### 3.1 Fundamentals

- 🐷 Attention
- 👨👩👧👦 Pre-trained Models

### 3.2 Information Extraction

#### 3.2.1 Sequence Labelling

- 🐷 BiLSTM+CRF
- 🐶 Hierarchically-Refined Label Attention Network for Sequence Labeling (EMNLP 2019, BiLSTM-LAN)
- 🐶 Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (ACL 2018)
- 🐶 Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (ACL 2020, L-TapNet + CDT)
- 🐶 Low Resource Sequence Tagging using Sentence Reconstruction (ACL 2020)

#### 3.2.2 Named Entity Recognition

- 🐶 Simplify the Usage of Lexicon in Chinese NER (ACL 2020)
- 🐶 Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (EMNLP 2020, StructShot)

#### 3.2.3 Relation Extraction

##### – Few-Shot RE

- 🐶 Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification (AAAI 2019, Proto-Hatt)
- 🐶 Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (ACL 2019, MLMAN)
- 🐶 Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (COLING 2020, CTEG)
- 🐶 Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (COLING 2020, MIML)
- 🐶 A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (COLING 2020, IncreProtoNet)
- 🐶 Prototypical Representation Learning for Relation Extraction (ICLR 2021, COL)

##### – Document RE

- 👨👩👧👦 Paper List (private for the time being)
- 🐶 Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (EMNLP 2019, EoG)
- 🐶 Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (ACL 2020, LSR)
- 🐶 Double Graph Based Reasoning for Document-level Relation Extraction (EMNLP 2020, GAIN)
- 🐶 Global-to-Local Neural Networks for Document-Level Relation Extraction (EMNLP 2020, GLRE)
- 🐶 Document-level Relation Extraction with Dual-tier Heterogeneous Graph (COLING 2020, DHG)
- 🐶 Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (COLING 2020, GEDA)
- 🐶 Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (COLING 2020, GCGCN)
- 🐶 Coarse-to-Fine Entity Representations for Document-level Relation Extraction (arXiv 2020, CFER)
- 🐶 Multi-view Inference for Relation Extraction with Uncertain Knowledge (AAAI 2021, MIUK)

##### – Others

- 🐶 Integrating Relation Constraints with Neural Relation Extractors (AAAI 2020, CLC)
- 🐶 Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction (COLING 2020, C-GCN-MG)

#### 3.2.4 Entity Relation Extraction

- 🐶 Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme (ACL 2017)
- 🐶 Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism (ACL 2018)
- 🐶 Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling (AAAI 2019)
- 🐶 Joint Type Inference on Entities and Relations via Graph Convolutional Networks (ACL 2019)
- 🐶 Entity-Relation Extraction as Multi-turn Question Answering (ACL 2019)
- 🐶 A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (ACL 2020, CasREL)
- 🐶 A Frustratingly Easy Approach for Joint Entity and Relation Extraction conference (arXiv 2020)
- 🐶 Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (COLING 2020, MPE)
- 🐶 Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (EMNLP 2020)

#### 3.2.5 Event Detection / Extraction

- 👨👩👧👦 Survey of event extraction
- 🐶 Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks (ACL 2015, DMCNN)
- 🐶 Joint Event Extraction via Recurrent Neural Networks (ACL 2016, JRNN)
- 🐶 A Convolution BiLSTM Neural Network Model for Chinese Event Extraction (NLPCC 2016, CNN + BiLSTM)
- 🐶 Scale Up Event Extraction Learning via Automatic Training Data Generation (AAAI 2018, BiLSTM + CRF + ILP + CVT)
- 🐶 Adversarial Training for Weakly Supervised Event Detection (NAACL 2019, Adv-ED)

#### 3.2.6 Intent Detection

- 🐶 Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference (EMNLP 2020, DNNC)

### 3.3 Representation Learning in NLP

- 🐶 Learning Structured Text Representations (TACL 2018)
- 🐶 Language Through a Prism: A Spectral Approach for Multiscale Language Representations (NeurIPS 2020)

### 3.4 Knowledge Graph

#### 3.4.1 Knowledge Representation Learning

- 🐶 Translating Embeddings for Modeling Multi-relational Data (NeurIPS 2013, TransE)

#### 3.4.2 Entity Alignment

- 🐶 Neighborhood Matching Network for Entity Alignment (ACL 2020, NMN)