ml-lab Goto Github PK

starchild

A library for verifying properties of neural networks using an SMT solver.

starcraftaitournamentmanager

Tournament Manager Software for StarCraft AI Competitions

stardata

Starcraft AI Research Dataset

stardist

Code for MICCAI 2018 paper "Cell Detection with Star-convex Polygons" (Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers)

stargan

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Tranlsation (PyTorch Implemenation)

stargan-tensorflow

Simple Tensorflow implementation of StarGAN (CVPR 2018 Oral)

stargan-voice-conversion

This is a pytorch implementation of the paper: StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

starmap

StarMap for Category-Agnostic Keypoint and Viewpoint Estimation

starspace

Learning embeddings for classification, retrieval and ranking.

stat453-deep-learning-ss20

STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2019)

state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

statistical-analysis-python-tutorial

Statistical Data Analysis in Python

statistical-rethinking-with-python-and-pymc3

Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

statrethinking_winter2019

Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019

steal-ml

Model extraction attacks on Machine-Learning-as-a-Service platforms.

stein-variational-gradient-descent

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

stellargraph

StellarGraph - Machine Learning on Graphs

stereo-magnification

Code accompanying the SIGGRAPH 2018 paper "Stereo Magnification: Learning View Synthesis using Multiplane Images"

stereo-pose-machines

stick-breaking-vae

Infinite MIxture Models with VAEs

stn.pytorch

pytorch version of spatial transformer networks

stnbhwd

Modules for spatial transformer networks (BHWD layout)

stochastic-cnn

Torch implementation for Robust convolutional neural networks under adversarial noise

stochastic-quantization

Training Low-bits DNNs with Stochastic Quantization

stochastic_depth

Code for Deep Networks with Stochastic Depth

stock_market_reinforcement_learning

This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stocktalk

Data collection toolkit for social media analytics

storyboarder

Storyboarder makes it easy to visualize a story as fast you can draw stick figures.

storygraphs

StoryGraphs -- Visualizing Character Interactions as a Timeline