67 Repositories
Python brewing-processes Libraries
This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)
GP-VAE This repository provides datasets and code for preprocessing, training and testing models for the paper: Diverse Text Generation via Variationa
Return-Parity-MDP - Towards Return Parity in Markov Decision Processes
Towards Return Parity in Markov Decision Processes Code for the AISTATS 2022 pap
MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?
MOT-Tracking-by-Detection-Pipeline Tracking-by-Detection形式のMOT(Multi Object Trac
This speeds up PyCharm's package index processes and avoids CPU & memory overloading
This speeds up PyCharm's package index processes and avoids CPU & memory overloading
On the adaptation of recurrent neural networks for system identification
On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape
A Python implementation of active inference for Markov Decision Processes
A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras
Use stochastic processes to generate samples and use them to train a fully-connected neural network based on Keras which will then be used to generate residuals
Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver
Designed a greedy algorithm based on Markov sequential decision-making process in MATLAB/Python to optimize using Gurobi solver, the wheel size, gear shifting sequence by modeling drivetrain constraints to achieve maximum laps in a race with a 2-hour time window.
Code for NeurIPS 2021 paper 'Spatio-Temporal Variational Gaussian Processes'
Spatio-Temporal Variational GPs This repository is the official implementation of the methods in the publication: O. Hamelijnck, W.J. Wilkinson, N.A.
Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control
Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control.
Used the pyautogui library to automate some processes on the computer
Pyautogui Utilizei a biblioteca pyautogui para automatizar alguns processos no c
Functional interface for concurrent futures, including asynchronous I/O.
Futured provides a consistent interface for concurrent functional programming in Python. It wraps any callable to return a concurrent.futures.Future,
JumpDiff: Non-parametric estimator for Jump-diffusion processes for Python
jumpdiff jumpdiff is a python library with non-parametric Nadaraya─Watson estimators to extract the parameters of jump-diffusion processes. With jumpd
Run python scripts and pass data between multiple python and node processes using this npm module
Run python scripts and pass data between multiple python and node processes using this npm module. process-communication has a event based architecture for interacting with python data and errors inside nodejs.
Pyeventbus: a publish/subscribe event bus
pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and
Self-Adaptable Point Processes with Nonparametric Time Decays
NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P
🍰 ConnectMP - An easy and efficient way to share data between Processes in Python.
ConnectMP - Taking Multi-Process Data Sharing to the moon 🚀 Contribute · Community · Documentation 🎫 Introduction : 🍤 ConnectMP is the easiest and
A desktop application developed in Python with PyQt5 to predict demand and help monitor and schedule brewing processes for Barnaby's Brewhouse.
brewhouse-management A desktop application developed in Python with PyQt5 to predict demand and help monitor and schedule brewing processes for Barnab
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).
The Neural Process Family This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CN
A Python package for faster, safer, and simpler ML processes
Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe
Scan all java processes on your host to check weather it's affected by log4j2 remote code execution
Log4j2 Vulnerability Local Scanner (CVE-2021-45046) Log4j 漏洞本地检测脚本,扫描主机上所有java进程,检测是否引入了有漏洞的log4j-core jar包,是否可能遭到远程代码执行攻击(CVE-2021-45046)。上传扫描报告到指定的服
Unsynchronize asyncio by using an ambient event loop, or executing in separate threads or processes.
unsync Unsynchronize asyncio by using an ambient event loop, or executing in separate threads or processes. Quick Overview Functions marked with the @
Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.
Flyte Flyte is a workflow automation platform for complex, mission-critical data, and ML processes at scale Home Page · Quick Start · Documentation ·
Deep Latent Force Models
Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona
This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression problems
Doctoral dissertation of Zheng Zhao This thesis is mainly concerned with state-space methods for a class of deep Gaussian process (DGP) regression pro
A synchronous, single-threaded interface for starting processes on Linux
A synchronous, single-threaded interface for starting processes on Linux
An Active Automata Learning Library Written in Python
AALpy An Active Automata Learning Library AALpy is a light-weight active automata learning library written in pure Python. You can start learning auto
Dense Gaussian Processes for Few-Shot Segmentation
DGPNet - Dense Gaussian Processes for Few-Shot Segmentation Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxi
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.
counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code
Set named timers for cooking, watering plants, brewing tea and more.
Timer Set named timers for cooking, watering plants, brewing tea and more. About Use Mycroft when your hands are messy or you need more that the one t
Code and real data for the paper "Counterfactual Temporal Point Processes", available at arXiv.
counterfactual-tpp This is a repository containing code and real data for the paper Counterfactual Temporal Point Processes. Pre-requisites This code
This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"
Prior-RObust Bayesian Optimization (PROBO) Introduction, TOC This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our
A python tool for synchronizing the messages from different threads, processes, or hosts.
Sync-stream This project is designed for providing the synchoronization of the stdout / stderr among different threads, processes, devices or hosts.
Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes'
Dual Parameterization of Sparse Variational Gaussian Processes Documentation | Notebooks | API reference Introduction This repository is the official
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.
Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil
A complete end-to-end machine learning portal that covers processes starting from model training to the model predicting results using FastAPI.
Machine Learning Portal Goal Application Workflow Process Design Live Project Goal A complete end-to-end machine learning portal that covers processes
PyTorch and GPyTorch implementation of the paper "Conditioning Sparse Variational Gaussian Processes for Online Decision-making."
Conditioning Sparse Variational Gaussian Processes for Online Decision-making This repository contains a PyTorch and GPyTorch implementation of the pa
Gorrabot is a bot made to automate checks and processes in the development process.
Gorrabot is a Gitlab bot made to automate checks and processes in the Faraday development. Features Check that the CHANGELOG is modified By default, m
Modular Gaussian Processes
Modular Gaussian Processes for Transfer Learning 🧩 Introduction This repository contains the implementation of our paper Modular Gaussian Processes f
Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks
Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi
LittleBrother is a simple parental control application monitoring specific processes on Linux hosts to monitor and limit the play time of children.
Parental Control Application LittleBrother Overview LittleBrother is a simple parental control application monitoring specific processes (read "games"
Bayesian Meta-Learning Through Variational Gaussian Processes
vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces
QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.
QuakeLabeler Quake Labeler was born from the need for seismologists and developers who are not AI specialists to easily, quickly, and independently bu
Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations.
BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer
Development of IP code based on VIPs and AADM
Sparse Implicit Processes In this repository we include the two different versions of the SIP code developed for the article Sparse Implicit Processes
A Tensorflow based library for Time Series Modelling with Gaussian Processes
Markovflow Documentation | Tutorials | API reference | Slack What does Markovflow do? Markovflow is a Python library for time-series analysis via prob
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)
Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje
This tool ability to analyze software packages of different programming languages that are being or will be used in their codes, providing information that allows them to know in advance if this library complies with processes.
This tool gives developers, researchers and companies the ability to analyze software packages of different programming languages that are being or will be used in their codes, providing information that allows them to know in advance if this library complies with processes. secure development, if currently supported, possible backdoors (malicious embedded code), typosquatting analysis, the history of versions and reported vulnerabilities (CVEs) of the package.
Official Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.
Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge: Official Pytorch implementation of ICLR 2018 paper Deep Learning for Phy
A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.
A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs, supporting both control flow and dataflow execution paradigms as well as de-centralized CPU & GPU scheduling.
Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".
Matern Gaussian Processes on Graphs This repo provides an extension for gpflow with Matérn kernels, inducing variables and trainable models implemente
Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)
Fast and Flexible Temporal Point Processes with Triangular Maps This repository includes a reference implementation of the algorithms described in "Fa
Newt - a Gaussian process library in JAX.
Newt __ \/_ (' \`\ _\, \ \\/ /`\/\ \\ \ \\
Socorro is the Mozilla crash ingestion pipeline. It accepts and processes Breakpad-style crash reports. It provides analysis tools.
Socorro Socorro is a Mozilla-centric ingestion pipeline and analysis tools for crash reports using the Breakpad libraries. Support This is a Mozilla-s
Gaussian processes in TensorFlow
Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow
A highly efficient and modular implementation of Gaussian Processes in PyTorch
GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
PyTorch Implementation of Differentiable SDE Solvers This library provides stochastic differential equation (SDE) solvers with GPU support and efficie
Official code for the ICLR 2021 paper Neural ODE Processes
Neural ODE Processes Official code for the paper Neural ODE Processes (ICLR 2021). Abstract Neural Ordinary Differential Equations (NODEs) use a neura
A colony of interacting processes
NColony Infrastructure for running "colonies" of processes. Hacking $ tox Should DTRT -- if it passes, it means unit tests are passing, and 100% cover
Safe Bayesian Optimization
SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p
A Python implementation of global optimization with gaussian processes.
Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat
A highly efficient and modular implementation of Gaussian Processes in PyTorch
GPyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes
Fast and Easy Infinite Neural Networks in Python
Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural
Inject code into running Python processes
pyrasite Tools for injecting arbitrary code into running Python processes. homepage: http://pyrasite.com documentation: http://pyrasite.rtfd.org downl
Machine learning, in numpy
numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install
Debugger capable of attaching to and injecting code into python processes.
DISCLAIMER: This is not an official google project, this is just something I wrote while at Google. Pyringe What this is Pyringe is a python debugger