Papers
1.CogLTX (작성중)
CogLTX: Applying BERT to Long Texts Ding et al, NeurIPS 2020
2.CogLTX 2
test
3.Attention is all you need, NeurIPS 2017
Attention is all you needsequence modeling and transduction problem을 잘 푸는 것Constraint of sequential computationBased on Self-Attention mechanism (disc
4.Improving Language Understanding by Generative Pre-Training, 2018
Improving Language Understanding by Generative Pre-Training, Technical report, OpenAI, 2018Learn a universal representation that transfers with little
5.BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, NAACL 2019Propose powerful pretrained LM for general language repres
6.Graph Convolutional Neural Networks for Web-Scale Recommender Systems, KDD 18
Graph Convolutional Neural Networks for Web-Scale Recommender Systems, KDD 18Generate high-quality embeddings/reps. of pins(items) that can be used fo
7.Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation, 2020 SIGIR
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation, 2020 SIGIRTransGRecPersonalized video highlight RecommendationDifficul
8.Inductive Representation Learning on Large Graphs, Neurips 2017
Inductive Representation Learning on Large Graphs, Neurips 2017GraphSAGEGCN-based inductive node embedding problemtransductive models cannot generaliz
9.STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems, IJCAI 19, Zhang et al
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems, IJCAI 19, Zhang et alGoalInductive(and Transductive) User-It