auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning.
If you use the code, please kindly cite the following paper:
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, Xuan Zhu. Learning Entity and Relation Embeddings for Knowledge Graph Completion. The 29th AAAI Conference on Artificial Intelligence (AAAI'15).
In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
Relation extraction on an open-domain knowledge base
Accompanying repository for our EMNLP 2017 paper. It contains the code to replicate the experiments and the pre-trained models for sentence-level relation extraction.
Labeled LDA (D. Ramage, D. Hall, R. Nallapati and C.D. Manning; EMNLP2009) is a supervised topic model derived from LDA (Blei+ 2003). While LDA's estimated topics don't often equal to human's expectation because it is unsupervised, Labeled LDA is to treat documents with multiple labels. I implemented Labeled LDA in python.
Stan modeling language and C++ library for Bayesian inference. NUTS adaptive HMC (MCMC) sampling, automatic differentiation, R, shell interfaces. Gelman.
Event-Detection - DBSCAN Algorithm in Map/Reduce logic, implemented with Hadoop and MongoDB, to analyze tweets and photos and to create geolocated events