Pinned Repositories
AI-Conference-Info
Extensive acceptance rates and information of main AI conferences
AnalyzeKGE
Analyzing knowledge graph embedding methods, including TransE, DistMult, CP, SimplE, ComplEx, Quaternion
CitationAnalysis
Citation Analysis on the Microsoft Academic Graph Dataset
KG20C
A Scholarly Knowledge Graph Benchmark Dataset
MEI-KGE
High-performance implementations of W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, and MEI. Paper: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion (ECAI 2020).
MEIM-KGE
Including W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, MEI, MEIM. Paper: MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction (IJCAI 2022).
PaperRecommender
Scientific Paper Recommender Systems.
Parallel_Topic_Modeling
Efficient Parallel Time-aware Topic Modeling on Multi-core CPU.
tranhungnghiep's Repositories
tranhungnghiep/AI-Conference-Info
Extensive acceptance rates and information of main AI conferences
tranhungnghiep/AnalyzeKGE
Analyzing knowledge graph embedding methods, including TransE, DistMult, CP, SimplE, ComplEx, Quaternion
tranhungnghiep/MEIM-KGE
Including W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, MEI, MEIM. Paper: MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction (IJCAI 2022).
tranhungnghiep/KG20C
A Scholarly Knowledge Graph Benchmark Dataset
tranhungnghiep/Parallel_Topic_Modeling
Efficient Parallel Time-aware Topic Modeling on Multi-core CPU.
tranhungnghiep/MEI-KGE
High-performance implementations of W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, and MEI. Paper: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion (ECAI 2020).
tranhungnghiep/CitationAnalysis
Citation Analysis on the Microsoft Academic Graph Dataset
tranhungnghiep/PaperRecommender
Scientific Paper Recommender Systems.
tranhungnghiep/Awesome-Open-Problems-for-ML
A curated list of awesome open problems and datasets calling for research using machine learning tools
tranhungnghiep/awesome-quantum-machine-learning
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
tranhungnghiep/blist
A list-like type with better asymptotic performance and similar performance on small lists
tranhungnghiep/char-rnn
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
tranhungnghiep/CoreNLP
Stanford CoreNLP: A Java suite of core NLP tools.
tranhungnghiep/corenlp-examples
Stanford Core NLP API usage examples
tranhungnghiep/COVID19Inspection
COVID-19 Inspection
tranhungnghiep/CSPublicationCrawler
Computer science scholarly data crawler
tranhungnghiep/deeplearning4nlp-tutorial
Hands-on tutorial on deep learning with a special focus on Natural Language Processing (NLP)
tranhungnghiep/fastText
Library for fast text representation and classification.
tranhungnghiep/gensim
Topic Modelling for Humans
tranhungnghiep/hyde_pixyll
Jekyll theme: Hyde sidebar + Pixyll clean style.
tranhungnghiep/Java-ML-Utils
A collection of useful utilities for machine learning and text mining in Java
tranhungnghiep/models
Models built with TensorFlow
tranhungnghiep/node2vec
tranhungnghiep/OpenKE
An Open-Source Package for Knowledge Embedding (KE)
tranhungnghiep/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
tranhungnghiep/SentEval
A python tool for evaluating the quality of sentence embeddings.
tranhungnghiep/simple-numpy-nn
Simple neural networks in NumPy for educational purpose with illustrative design choices and extensive comments
tranhungnghiep/StarSpace
Learning embeddings for classification, retrieval and ranking.
tranhungnghiep/tensor2tensor
A library for generalized sequence to sequence models
tranhungnghiep/TimeSeriesAnalysisWithPython