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2023-Summer-Internship-DSAIL

drawing

Data Science and Artificial Intelligence Laboratory (DSAIL)

Tentative Schedule

Basic recommender system and Graph Neural Network

Year Paper Short Implementation Review
2016 Semi-Supervised Classification with Graph Convolutional Networks GCN Review
2014 DeepWalk: Online Learning of Social Representations Deepwalk Implementation
2008 Probabilistic Matrix Factorization PMF Implementation
2009 Matrix Factorization Techniques for Recommender Systems Netflix Implementation Review
2008 Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model Netflix Implementation Review
2008 Collaborative Filtering for Implicit Feedback Datasets OCCF Review
2012 BPR: Bayesian Personalized Ranking from Implicit Feedback BPR Review
2013 Translating Embeddings for Modeling Multi-relational Data TransE Implementation Review
2017 Collaborative Metric Learning CML Review
2010 Factorization Machines FM Implementation Review
2016 Wide & Deep Learning for Recommender Systems WD Implementation Review
2008 SoRec: Social Recommendation Using Probabilistic Matrix Factorization SoRec Implementation
2011 Recommender Systems with Social Regularization SoReg
2017 metapath2vec : Scalable Representation Learning for Heterogeneous Networks Metapath2Vec
2018 Deep Graph Infomax DGI Implementation Review
2017 Inductive Representation Learning on Large Graphs GraphSAGE
2013 Auto-Encoding Variational Bayes VAE Implementation
2016 Variational Graph Auto-Encoders VGAE
2016 Deep Neural Networks for YouTube Recommendations Youtube Review
2015 AutoRec: Autoencoders Meet Collaborative Filtering Autorec Implementation Review
2015 Convolutional Matrix Factorization for Document Context-Aware Recommendation ConvMF Review
2017 Augmentation-Free Self-Supervised Learning on Graphs AFGRL
2017 Attention Is All You Need Transformer Implementation Review
2018 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding BERT Review

Participate to projects

  • MATH AI (Multi-modal Reasoning)
  • Adopting Large Language Models (LLMs) in Learning on Graphs

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