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@for-ai @VectorInstitute @UTMIST
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xwinxu/README.md

I research generative models and LLMs in collaboration with @Google-Brain and @StanfordAI. I'm a recent graduate of CS, Math, and Stats at the University of Toronto where I worked on latent variable models, probabilistic inference, and neural ODEs/SDE at the Vector Institute. I'm seeking PhD oppys for 2023.

Previously, I scaled Transfomers on human-generated content at @Cohere.ai and developed deep learning tools for robotics @NVIDIA. Like my models, I'm still learning (:

Pinned

  1. bayeSDE Public

    Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

    Python 125 20

  2. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

    Jupyter Notebook 11

  3. Python library which enables complex compositions of language models such as scratchpads, chain of thought, tool use, selection-inference, and more.

    Python 81 6

  4. Fitting a recognition model (VAE) to do approximate inference on intractable posteriors of probabilistic models using an ELBO estimator.

    Python 7 1

  5. Generating structured sparsity in neural networks using weight and unit pruning techniques.

    Python 1

  6. Predicting transcription factor binding sites using next-generation sequencing genomic data and Segway.

    R

609 contributions in the last year

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Mon Wed Fri
Activity overview
Contributed to xwinxu/xwinxu.github.io, xwinxu/dotfiles, xwinxu/bayeSDE and 10 other repositories

Contribution activity

October 2022

5 contributions in private repositories Oct 2 – Oct 4

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