shayneobrien/text-cluster

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Created about 6 years ago
Updated about 6 years ago

shayneobrien/text-cluster

shayneobrien

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Offline and online (i.e., real-time) annotated clustering methods for text data.

generative-models

Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Jupyter Notebook

coreference-resolution

Efficient and clean PyTorch reimplementation of "End-to-end Neural Coreference Resolution" (Lee et al., EMNLP 2017).

numerical-methods

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.

language-modeling

Language modeling on the Penn Treebank (PTB) corpus using a trigram model with linear interpolation, a neural probabilistic language model, and a regularized LSTM.

Jupyter Notebook

sentiment-classification

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.

Jupyter Notebook

explicit-gan-eval

Code for reproducing the results of "Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions" (O'Brien et al., NeurIPS 2018).

text-segmentation

Neural and nonneural text segmentation methods.

Jupyter Notebook

machine-translation

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.

Jupyter Notebook

ml-from-scratch

Implementations of basic machine learning algorithms using only numpy, pandas, and matplotlib.

Jupyter Notebook

selected-papers-and-presentations

A sample of selected papers that I have authored or co-authored.

conversational-analysis

Unsupervised methods for analysis of conversational transcripts

lexrank

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.