helingzhang Goto Github PK

coqgym

A Learning Environment for Theorem Proving with the Coq proof assistant

coqproject

Bash script for simple generation of Coq project metadata files

firmware

PX4 Autopilot Software

graph-generation

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

mavros_offboard_control

MAVROS offboard control

nao

Neural Architecture Optimization

netgan

Implementation of the paper "NetGAN: Generating Graphs via Random Walks".

nmp_qc

Neural Message Passing for Computer Vision

prednet

Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"

predrnn-pp

Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)

ros_dronemap

spiking-neural-network

Pure python implementation of SNN

spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

voidrice

My dotfiles (deployed by LARBS)