Name: Prince
Type: User
Company: Carnegie Mellon University
Bio: Doing AI research, including but not limited to, machine learning, statistical learning theory, variational inference
Location: California, Santa Barbara
Blog: https://kingofspace0wzz.github.io
Prince's Projects
Sequence to Sequence Models with PyTorch
Project report for "Spectral Graph Partitioning and its application in Image Segmentation"
A shallow fork of SuiteSparse adding build files for Visual Studio and support for ACML
PyTorch implementation of Super SloMo by Jiang et al.
t-SNE experiments in pytorch
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
UAI2020 Fairness paper
Website: https://ucsb-ccs-computing.github.io. See also: https://ucsb-cs-ce-profiles.github.io
Latent Alignment and Variational Attention
Implementation of SVRG and SAGA optimization algorithms for deep learning topics.
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
Code for EMNLP18 paper "Spherical Latent Spaces for Stable Variational Autoencoders"
Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" https://arxiv.org/abs/1904.02399
Various tutorials given for welcoming new students at MILA.