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

    Jupyter Notebook 466 72

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

    Perl 179 58

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

    Python 10 1

  4. 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, …

    MATLAB 24 10

  5. 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 3 1

  6. 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 14 3

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October 2022

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