mods333 Goto Github PK

3d-sis

[CVPR'19] 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

anglican

Probabilistic Programming System Anglican

assignment

cpp-algoritms

c++ codes and algorithms for some of the most common problems

deepul

diverse-image-synthesis-from-semantic-layout

dotfiles

energy-based-scene-graph

Code release for Energy-Based Learning for Scene Graph Genertaion

energy_based_generative_models

PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models

equivalence_checker_javascript

Given a program and its optimzed version (created using prepack), check if the two programs are equivalent

few-shot-music-generation

fomg

foppl

google-research

Google Research

graph-rcnn.pytorch

Pytorch code for our ECCV 2018 paper "Graph R-CNN for Scene Graph Generation" and other papers

hmc_pytroch

An implementation of Hamiltonian Monte Carlo using pytorch

jem-1

Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"

kickstart.nvim

A launch point for your personal nvim configuration

mods333.github.io

Minimal is a Jekyll theme for GitHub Pages

nerf

Code release for NeRF (Neural Radiance Fields)

nvim

pygat

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

python_programs

pytorch_examples

rat-spn

Code for UAI'19: Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

scene-graph-benchmark.pytorch

A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”

spinningup

An educational resource to help anyone learn deep reinforcement learning.

ubc_cvrg

UBC Computer Vision Research website (development)

verb-attributes

Contains code for the EMNLP paper `Learning Linguistic Attributes for Zero-Shot Verb Classification'

website