shayneobrien Goto Github PK
Name: Shayne O'Brien
Type: User
Company: MIT
Bio: NLP / Machine Learning / Network Science. Moved from MIT to Apple 06/2019
Location: San Francisco, CA
Name: Shayne O'Brien
Type: User
Company: MIT
Bio: NLP / Machine Learning / Network Science. Moved from MIT to Apple 06/2019
Location: San Francisco, CA
Unsupervised methods for analysis of conversational transcripts
Efficient and clean PyTorch reimplementation of "End-to-end Neural Coreference Resolution" (Lee et al., EMNLP 2017).
Visually explore how the meaning of words changes over time.
Code for reproducing the results of "Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions" (O'Brien et al., NeurIPS 2018).
:wolf: K-Means Clustering using Python from Scratch :mushroom:
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Language modeling on the Penn Treebank (PTB) corpus using a trigram model with linear interpolation, a neural probabilistic language model, and a regularized LSTM.
LexRank for ranking documents containing some keyword or keyphrase using cosine similarities of either naive, tfidf, or idf-modified-cosine. Non-query ranking also supported.
Visualizations that I created while at Clemson University developing methods for the visualization of light detection and ranging (LIDAR) data.
Neural machine translation on the IWSLT-2016 dataset of Ted talks translated between German and English using sequence-to-sequence models with/without attention and beam search.
Implementations of basic machine learning algorithms using only numpy, pandas, and matplotlib.
Methods in numerical analysis. Includes: Lagrange interpolation, Chebyshev polynomials for optimal node spacing, iterative techniques to solve linear systems (Gauss-Seidel, Jacobi, SOR), SVD, PCA, and more.
Solutions that I have coded to a variety of projecteuler.net problems in Python and MATLAB.
A sample of selected papers that I have authored or co-authored.
A rule-based system to compress simple sentence using nltk
Neural sentiment classification of text using the Stanford Sentiment Treebank (SST-2) movie reviews dataset, logistic regression, naive bayes, continuous bag of words, and multiple CNN variants.
An Open Source Machine Learning Framework for Everyone
Offline and online (i.e., real-time) annotated clustering methods for text data.
Neural and nonneural text segmentation methods.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.