Jake Graving's repositories
rna-seq-tsne
The art of using t-SNE for single-cell transcriptomics
openTSNE
Extensible, parallel implementations of t-SNE
bb_binary
Binary Format for the BeesBook detections
bb_pipeline
Social networks in honeybees - Detection pipeline
bb_tracking
Python code to perform tracking and evaluate the performance of tracking algorithms for the beesbook project.
Deep-Learning-Experiments
Notes and experiments to understand deep learning concepts
DeepLabCut-Workshop-Materials
Workshop material for using DeepLabCut
deepposekit-paper
code and files for the DeepPoseKit manuscript
DLCutils
Various scripts to support deeplabcut...
Docker4DeepLabCut2.0
Docker container for running DeepLabCut2.0 (linux support only)
IntegratedGradients
Python/Keras implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.
joint-vae
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation :star2:
leap
LEAP Estimates Animal Pose
Learning-aftershock-location-patterns
Training, testing, evaluation codes for learning aftershock location patterns
normalizing-flows-tutorial
Tutorial on normalizing flows.
opencv
Open Source Computer Vision Library
pixel-cnn
Code for the paper "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"
polar-transformer-networks
Demo source code for the paper "Esteves, C., Allen-Blanchette, C., Zhou, X. and Daniilidis, K, "Polar Transformer Networks", ICLR 2018.
pytorch-made
MADE (Masked Autoencoder Density Estimation) implementation in PyTorch
pytorch-pose
A PyTorch toolkit for 2D Human Pose Estimation.
pytorch-vq-vae
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
pytorch-vqvae
Vector Quantized VAEs - PyTorch Implementation
sandbox
Play time!
Stacked_Hourglass_Network_Keras
Keras Implementation for Stacked Hourglass Network
STN.keras
Implementation of spatial transformer networks (STNs) in keras 2 with tensorflow as backend.
world-models
Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch