awesome-ai
Awesome list for all things AI, ML and deep learning
https://github.com/MRCIEU/awesome-ai
Last synced: 20 days ago
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Awesome lists
Awesome papers
- awesome-deep-learning-papers
- awesome-decision-tree-papers
- awesome-gradient-boosting-papers
- must read papers on graph neural network
- deep learning biology
- awesome-deepbio
Frameworks / ecosystems
- awesome-ternsorflow
- awesome-pytorch-list
- the-incredible-pytorch
- keras-resources
- DataSciencePython
- DataScienceR
General
Graph theory
- awesome-network-analysis
- awesome-community-detection
- awesome-graph-classification
- awesome-network-embedding
- graph-based-deep-learning-literature
- LiteratureDL4Graph
Knowledge graph
- totogo/awesome-knowledge-graph
- BrambleXu/knowledge-graph-learning
- husthuke/awesome-knowledge-graph (in Chinese)
- shaoxiongji/awesome-knowledge-graph
NLP
- NCBI BioNLP Research Group (PI: Zhiyong Lu)
- thunlp
- the super duper NLP repo
- nlp-roadmap
- NLP-progress
- awesome-nlp
- awesome-bert
- awesome-word2vec
- awesome-embedding-models
- neural relation extraction (NRE)
- machine reading comprehension
- network representation learning (NRL) / network embedding (NE)
- knowledge representation learning (KRL) / knowledge embedding (KE)
Notebooks
Research papers / blog articles
NLP: applications
- Wang et al., 2018. Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
- Wang et al., 2019., Extracting evidence of supplement-drug interactions from literature
- Wang et al., 2018. Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
- Wang et al., 2019., Extracting evidence of supplement-drug interactions from literature
NLP: methods
- Parikh, A.P., Täckström, O., Das, D. and Uszkoreit, J., 2016. A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
- The illustrated GPT-2 (Visulaizing Transformer Language Models)
- Searching for Answer Candidate Passages with Solr and Anserini
- Subramanian et al., 2019. On extractive and abstractive neural document summarization with transformer language models
- Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).
- Searching for Answer Candidate Passages with Solr and Anserini
Transformer applications
Tooling
graph
- dgl: Python package built to ease deep learning on graph, on top of existing DL frameworks.
- cdlib: Community Discovery Library - (for networkx and igraph)
- Embedding-Vis
- OpenNE