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36705-intermediate-stat

36-705 Intermediate Statistics, Fall 2017 - CMU Statistics

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

caffe

Caffe: a fast framework for deep learning. For the most recent version checkout the dev branch. For the latest stable release checkout the master branch.

continuous-ppo-grad

deeplearning-papernotes

Summaries and notes on Deep Learning research papers

deligan

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.

deq

[NeurIPS'19] Deep Equilibrium Models

diff-qp-mpc

dmcgym

dmcgym without mujoco-py dependency

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

draw

TensorFlow Implementation of "DRAW: A Recurrent Neural Network For Image Generation"

exp_maml

Code for "Learning Exploration Strategies for Model Agnostic Meta-Reinforcement Learning", ICML AMTL workshop

labml

Organize PyTorch Experiments

lecture-notebooks-2022

lsd_slam

LSD-SLAM

m3rl

Mind-aware Multi-agent Management Reinforcement Learning

opencv

Open Source Computer Vision Library

orb_slam

A Versatile and Accurate Monocular SLAM

paws

Implementation for "Exploiting Data and Human Knowledge for Predicting Wildlife Poaching". (COMPASS 2018)

paws-compass

Implementation for "Exploiting Data and Human Knowledge for Predicting Wildlife Poaching". (COMPASS 2018)

pgm-spring-2019

Course webpage for PGM, Spring 2019.

rlbench

rpg_dvs_ros

ROS packages for DVS

rpg_svo

Semi-direct Visual Odometry

simurlacra

reinforcement learning from randomized simulations

sophus

C++ implementation of Lie Groups using Eigen.

stanford_dl_ex

Programming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial

swami1995.github.io

Personal Website

torch7

v2v

Code for "From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers"