cvoelcker

Awesome-CV

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

clonecademy

This is the BP origin of clonecademy. If you want to contribute to the current project, go to https://github.com/msusenburger/clonecademy

DeepNotebooks

DeepNotebooks is an automated statistical analysis tool build on top of SPNs. They are currently being developed by Claas Völcker at the ML group at TU Darmstadt.

genesis

Official PyTorch implementation of "GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations"

hero-manager

A django application to manage your rpg gaming group

master_experiments

MONet

An implementation of the MONet model for unsupervised scene decomposition in PyTorch

reinforcement-learning-material-ws-2018

This repository collects supplementary material to study reinforcement learning with a focus on topics covered by the TU Darmstadt IAS lecture on reinforcement learning.

sequential_inference

A repository for sequence models build on amortized variational inference in pytorch

variational_autoencoder_talk

A talk I've given on variational autoencoders. My first ever lecture

cvoelcker/reinforcement-learning-material-ws-2018

This repository collects supplementary material to study reinforcement learning with a focus on topics covered by the TU Darmstadt IAS lecture on reinforcement learning.

cvoelcker/DeepNotebooks

DeepNotebooks is an automated statistical analysis tool build on top of SPNs. They are currently being developed by Claas Völcker at the ML group at TU Darmstadt.

cvoelcker/Awesome-CV

:page_facing_up: Awesome CV is LaTeX template for your outstanding job application

cvoelcker/genesis

Official PyTorch implementation of "GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations"

cvoelcker/master_experiments

cvoelcker/variational_autoencoder_talk

A talk I've given on variational autoencoders. My first ever lecture

cvoelcker/IsaacGymEnvs

Isaac Gym Reinforcement Learning Environments

cvoelcker/MONet

An implementation of the MONet model for unsupervised scene decomposition in PyTorch

cvoelcker/PyConfigMaker

A yaml parser which automatically writes and reads config files to easily usable python namedtuples:w

cvoelcker/sequential_inference

A repository for sequence models build on amortized variational inference in pytorch

cvoelcker/vagram_quadratic

cvoelcker/alm

Simplifying Model-based RL: Learning Representations, Latent-space Models and Policies with One Objective

cvoelcker/auto_yolo

TensorFlow implementation of Spatially Invariant Attend, Infer, Repeat (SPAIR).

cvoelcker/blog

This is my blog currently accessible under https://cvoelcker.de

cvoelcker/CompoSuite

Official release of CompoSuite, a compositional RL benchmark

cvoelcker/dmc2gym

OpenAI Gym wrapper for the DeepMind Control Suite

cvoelcker/exPy

Experimentation setup and results management framework for machine learning approaches

cvoelcker/input-inference-for-control

Input Inference for Control (i2c), a control-as-inference framework for optimal control

cvoelcker/jax_modules

Collection of useful jax snippets and modules to build more complex differentiable code

cvoelcker/mbrl-lib

Library for Model Based RL

cvoelcker/offline-compositional-rl-datasets

cvoelcker/PyTorchRunner

A leightweight framework for experiment handling in pytorch

cvoelcker/SPFlow

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

cvoelcker/spn-pytorch-experiments

Experiments on the integration of Sum-Product Networks into Deep Neural Networks.

cvoelcker/spr

Code for "Data-Efficient Reinforcement Learning with Self-Predictive Representations"

cvoelcker/sqair

Implementation of Sequential Attend, Infer, Repeat (SQAIR)

cvoelcker/STOVE

Structured Object-Aware Physics Prediction for Video Modeling and Planning

cvoelcker/STOVE-1

Anonymous ICLR 2020 Submission: Structured Object-Aware Physics Prediction for Video Modelling and Planning

cvoelcker/uoft-cs-robotics.github.io

University of Toronto cs robotics group

cvoelcker/variational-smc

Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)