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I research scalable and principled methods for aligning large generative models. In the past, I worked at FAIR Labs at Meta, Google DeepMind and Stanford AI. I'm a graduate of CS, Math, and Stats at the University of Toronto where I began learning about latent variable models and probabilistic inference at the Vector Institute. I'm open to full-time ML research and engineering roles.

Like my models, I'm still learning (:

Winnie Xu's Projects

pruning-neural-nets icon pruning-neural-nets

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

rules_docker icon rules_docker

Rules for building and handling Docker images with Bazel

score_sde icon score_sde

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

social-bit icon social-bit

A system and visualization tool to track and summarize daily social encounters, i.e. a social "FitBIT".

softlearning icon softlearning

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

squeezedet icon squeezedet

A tensorflow implementation for SqueezeDet, a convolutional neural network for object detection.

tensorflow icon tensorflow

An Open Source Machine Learning Framework for Everyone

tmux-config icon tmux-config

Tmux configuration, that supercharges your tmux to build cozy and cool terminal environment

variational-mnist icon variational-mnist

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

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