498 Repositories
Python black-box-bayesian-inference Libraries
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.
Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio
Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).
What is judgyprophet? judgyprophet is a Bayesian forecasting algorithm based on Prophet, that enables forecasting while using information known by the
CLOOB training (JAX) and inference (JAX and PyTorch)
cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train
Code release for "BoxeR: Box-Attention for 2D and 3D Transformers"
BoxeR By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-A
Second Order Optimization and Curvature Estimation with K-FAC in JAX.
KFAC-JAX - Second Order Optimization with Approximate Curvature in JAX Installation | Quickstart | Documentation | Examples | Citing KFAC-JAX KFAC-JAX
Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!
SageMaker Studio Lab Sample Notebooks Available today in public preview. If you are looking for a no-cost compute environment to run Jupyter notebooks
Easy-to-use library to boost AI inference leveraging state-of-the-art optimization techniques.
NEW RELEASE How Nebullvm Works • Tutorials • Benchmarks • Installation • Get Started • Optimization Examples Discord | Website | LinkedIn | Twitter Ne
My Solutions to 120 commonly asked data science interview questions.
Data_Science_Interview_Questions Introduction 👋 Here are the answers to 120 Data Science Interview Questions The above answer some is modified based
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations
💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily install with pip.
Implementation of CaiT models in TensorFlow and ImageNet-1k checkpoints. Includes code for inference and fine-tuning.
CaiT-TF (Going deeper with Image Transformers) This repository provides TensorFlow / Keras implementations of different CaiT [1] variants from Touvron
Python code to control laboratory hardware and perform Bayesian reaction optimization on the MIT Make-It system for chemical synthesis
Description This repository contains code accompanying the following paper on the Make-It robotic flow chemistry platform developed by the Jensen Rese
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da
An official repository for tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a University of Edinburgh master's course.
PMR computer tutorials on HMMs (2021-2022) This is a repository for computer tutorials of Probabilistic Modelling and Reasoning (2021/2022) - a Univer
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools
Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t
Includes PyTorch - Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.
ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo
Materials to reproduce our findings in our stories, "Amazon Puts Its Own 'Brands' First Above Better-Rated Products" and "When Amazon Takes the Buy Box, it Doesn’t Give it up"
Amazon Brands and Exclusives This repository contains code to reproduce the findings featured in our story "Amazon Puts Its Own 'Brands' First Above B
In this tutorial, you will perform inference across 10 well-known pre-trained object detectors and fine-tune on a custom dataset. Design and train your own object detector.
Object Detection Object detection is a computer vision task for locating instances of predefined objects in images or videos. In this tutorial, you wi
Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)
ProbAI 2021 - Probabilistic Programming and Variational Inference Tutorial with Pryo Day 1 (June 14) Slides Notebook: students_PPLs_Intro Notebook: so
Official Pytorch implementation of Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR 2022)
The Official Implementation of CLIB (Continual Learning for i-Blurry) Online Continual Learning on Class Incremental Blurry Task Configuration with An
Opinionated code formatter, just like Python's black code formatter but for Beancount
beancount-black Opinionated code formatter, just like Python's black code formatter but for Beancount Try it out online here Features MIT licensed - b
Semi-automated OpenVINO benchmark_app with variable parameters
Semi-automated OpenVINO benchmark_app with variable parameters. User can specify multiple options for any parameters in the benchmark_app and the progam runs the benchmark with all combinations of given options.
This is an official implementation for "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"
DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation This repo is the official implementation of "DeciWatch: A Simple Baseline for
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).
SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo
Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)
A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M
Experimental Python implementation of OpenVINO Inference Engine (very slow, limited functionality). All codes are written in Python. Easy to read and modify.
PyOpenVINO - An Experimental Python Implementation of OpenVINO Inference Engine (minimum-set) Description The PyOpenVINO is a spin-off product from my
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
ZEROGEN This repository contains the code for our paper “ZeroGen: Efficient Zero
Lightweight mmm - Lightweight (Bayesian) Media Mix Model
Lightweight (Bayesian) Media Mix Model This is not an official Google product. L
Hierarchical-Bayesian-Defense - Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference (Openreview)
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical V
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the
Over-the-Air Ensemble Inference with Model Privacy
Over-the-Air Ensemble Inference with Model Privacy This repository contains simulations for our private ensemble inference method. Installation Instal
Kglab - an abstraction layer in Python for building knowledge graphs
Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, RDFlib, pySHACL, RAPIDS, NetworkX, iGraph, PyVis, pslpython, pyarrow, etc.
Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed
fast-Bart Reduction of BART model size by 3X, and boost in inference speed up to 3X BART implementation of the fastT5 library (https://github.com/Ki6a
A Python implementation of red-black trees
Python red-black trees A Python implementation of red-black trees. This code was originally copied from programiz.com, but I have made a few tweaks to
Python package for concise, transparent, and accurate predictive modeling
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Posterior temperature optimized Bayesian models for inverse problems in medical imaging Max-Heinrich Laves*, Malte Tölle*, Alexander Schlaefer, Sandy
Official Implementation of "Transformers Can Do Bayesian Inference"
Official Code for the Paper "Transformers Can Do Bayesian Inference" We train Transformers to do Bayesian Prediction on novel datasets for a large var
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations
Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Probabilistic U-Net + **Update** + An improved Model (the Hierarchical Probabilistic U-Net) + LIDC crops is now available. See below. Re-implementatio
Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
Human-Segmentation-PyTorch Human segmentation models, training/inference code, and trained weights, implemented in PyTorch. Supported networks UNet: b
Data-depth-inference - Data depth inference with python
Welcome! This readme will guide you through the use of the code in this reposito
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates
GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online Debates Vibhor Agarwal, Sagar Joglekar, Anthony P. Young an
Beyond imagenet attack (accepted by ICLR 2022) towards crafting adversarial examples for black-box domains.
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022) This is the Pytorch code for our paper Beyond ImageNet
Python implementation of Weng-Lin Bayesian ranking, a better, license-free alternative to TrueSkill
Python implementation of Weng-Lin Bayesian ranking, a better, license-free alternative to TrueSkill This is a port of the amazing openskill.js package
Box - a world simulator written in python with pygame
Box is a world simulator written in python with pygame. Features A world generation system A world editor Simulates creatures called boxlanders. You c
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing
This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U
Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains)
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022) This is the Pytorch code for our paper Beyond ImageNet
Accelerating BERT Inference for Sequence Labeling via Early-Exit
Sequence-Labeling-Early-Exit Code for ACL 2021 paper: Accelerating BERT Inference for Sequence Labeling via Early-Exit Requirement: Please refer to re
It was created to conveniently respond to events such as donation, follow, and hosting using the Alert Box provided by twip to streamers
This library is not an official library of twip. It was created to conveniently respond to events such as donation, follow, and hosting using the Alert Box provided by twip to streamers.
This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Koltun"
Learning to propose objects This implements the learning and inference/proposal algorithm described in "Learning to Propose Objects, Krähenbühl and Ko
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Natural Posterior Network This repository provides the official implementation o
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors).
An curated collection of awesome resources about networking in cybersecurity
An ongoing curated collection of awesome software, libraries, frameworks, talks & videos, best practices, learning tutorials and important practical resources about networking in cybersecurity
Bayesian Inference Tools in Python
BayesPy Bayesian Inference Tools in Python Our goal is, given the discrete outcomes of events, estimate the distribution of categories. Using gradient
Cereal box identification in store shelves using computer vision and a single train image per model.
Product Recognition on Store Shelves Description You can read the task description here. Report You can read and download our report here. Step A - Mu
Black-Scholes library implemented as a Cairo smart contract
Cairo Black-Scholes Library Black-Scholes library implemented as a Cairo smart contract. All inputs, outputs, and internal calculations use 27-digit f
Efficient Online Bayesian Inference for Neural Bandits
Efficient Online Bayesian Inference for Neural Bandits By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy AISTATS 2022.
A work in progress box containing various Python utilities
python-wipbox A set of modern Python libraries under development to simplify the execution of reusable routines by different projects. Table of Conten
A set of decks and notebooks with exercises for use in a hands-on causal inference tutorial session
intro-to-causal-inference A introduction to causal inference using common tools from the python data stack Table of Contents Getting Started Install g
Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data
VIMuRe Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. If you use this code please cite this article (preprint). De
Bayesian A/B testing
bayesian_testing is a small package for a quick evaluation of A/B (or A/B/C/...) tests using Bayesian approach.
High dimensional black-box optimizer using Latent Action Monte Carlo Tree Search algorithm
LA-MCTS The code is based of paper Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. Component LA-MCTS has thr
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection This material is supplementray code for paper accepted in ICDAR 2021 We h
Laplace Redux -- Effortless Bayesian Deep Learning
Laplace Redux - Effortless Bayesian Deep Learning This repository contains the code to run the experiments for the paper Laplace Redux - Effortless Ba
Box CRUD API With Python
Box CRUD API: Consider a store which has an inventory of boxes which are all cuboid(which have length breadth and height). Each Cuboid has been added
Source code for paper "Black-Box Tuning for Language-Model-as-a-Service"
Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a-Service". Being busy recently, the code in this repo and this tutoria
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition Introduction Run attack: SGADV.py Objective function: foolbox/attacks/gradi
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
Accurate Phylogenetic Inference with Symmetry-Preserving Neural Networks
Accurate Phylogenetic Inference with a Symmetry-preserving Neural Network Model Claudia Solis-Lemus Shengwen Yang Leonardo Zepeda-Núñez This repositor
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.
Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi
JWT Key Confusion PoC (CVE-2015-9235) Written for the Hack the Box challenge - Under Construction
JWT Key Confusion PoC (CVE-2015-9235) Written for the Hack the Box challenge - Under Construction This script performs a Java Web Token Key Confusion
Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service
Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a
Drf-stripe-subscription - An out-of-box Django REST framework solution for payment and subscription management using Stripe
Drf-stripe-subscription - An out-of-box Django REST framework solution for payment and subscription management using Stripe
Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks
Obs-Causal-Q-Network AAAI 2022 - Training a Resilient Q-Network against Observational Interference Preprint | Slides | Colab Demo | Environment Setup
Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"
LQVAE-separation Code for "Unsupervised Source Separation via Bayesian inference in the latent domain" Paper Samples GT Compressed Separated Drums GT
Bayesian Modeling and Computation in Python
Bayesian Modeling and Computation in Python Open access and Code This repository contains the open access version of the text and the code examples in
A webdav demo using a virtual filesystem that serves a random status of whether a cat in a box is dead or alive.
A webdav demo using a virtual filesystem that serves a random status of whether a cat in a box is dead or alive.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
PyPortfolioOpt has recently been published in the Journal of Open Source Software 🎉 PyPortfolioOpt is a library that implements portfolio optimizatio
A python tutorial on bayesian modeling techniques (PyMC3)
Bayesian Modelling in Python Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling t
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi
A general framework for inferring CNNs efficiently. Reduce the inference latency of MobileNet-V3 by 1.3x on an iPhone XS Max without sacrificing accuracy.
GFNet-Pytorch (NeurIPS 2020) This repo contains the official code and pre-trained models for the glance and focus network (GFNet). Glance and Focus: a
Natural Language Processing Best Practices & Examples
NLP Best Practices In recent years, natural language processing (NLP) has seen quick growth in quality and usability, and this has helped to drive bus
Log4jScanner is a Log4j Related CVEs Scanner, Designed to Help Penetration Testers to Perform Black Box Testing on given subdomains.
Log4jScanner Log4jScanner is a Log4j Related CVEs Scanner, Designed to Help Penetration Testers to Perform Black Box Testing on given subdomains. Disc
An API-first distributed deployment system of deep learning models using timeseries data to analyze and predict systems behaviour
Gordo Building thousands of models with timeseries data to monitor systems. Table of content About Examples Install Uninstall Developer manual How to
A new codebase for Group Activity Recognition. It contains codes for ICCV 2021 paper: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition and some other methods.
Spatio-Temporal Dynamic Inference Network for Group Activity Recognition The source codes for ICCV2021 Paper: Spatio-Temporal Dynamic Inference Networ
This Artificial Intelligence program can take a black and white/grayscale image and generate a realistic or plausible colorized version of the same picture.
Colorizer The point of this project is to write a program capable of taking a black and white / grayscale image, and generating a realistic or plausib
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
Think Bayes 2 by Allen B. Downey The HTML version of this book is here. Think Bayes is an introduction to Bayesian statistics using computational meth
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame
🏖 Keras Implementation of Painting outside the box
Keras implementation of Image OutPainting This is an implementation of Painting Outside the Box: Image Outpainting paper from Standford University. So
Software for quick purchase of mystery boxes on Binance.
english | русский язык Software for quick purchase of mystery boxes on Binance. Purpose Installation & setup Motivation Specification Disclaimer Purpo
It is a simple library to speed up CLIP inference up to 3x (K80 GPU)
CLIP-ONNX It is a simple library to speed up CLIP inference up to 3x (K80 GPU) Usage Install clip-onnx module and requirements first. Use this trick !
👨🏼💻 A customizable man-in-the-middle TCP proxy with out-of-the-box support for HTTP & HTTPS.
👨💻 mitm A customizable man-in-the-middle TCP proxy with out-of-the-box support for HTTP & HTTPS. Installing pip install mitm Note that OpenSSL 1.1
Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.
Introduction The goal of this analysis is to find a model that fits the observed cumulative cases of COVID-19 in the US, starting in Mid-July 2021 and
Catalogue CRUD Application
This Python program creates a relational SQL database hosted on the Snowflake platform, then opens a CRUD GUI to manipulate and view the data. In this application, it is used as a book catalogue. CURRENTLY WIP