Python Maximum-mean-discrepancy Resources

PS-MT [cvpr22] Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation by Yuyuan Liu, Yu Tian, Yuanhong Chen, Fengbei Liu, Vasile

Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape

MSPC for I2I This repository is by Yanwu Xu and contains the PyTorch source code to reproduce the experiments in our CVPR2022 paper Maximum Spatial Pe

About Code release for Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR 2022 Spotlight)

Documentation A python package to adjust the bias of probabilistic forecasts/hindcasts using "Mean and Variance Adjustment" method. Read documentation

Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

Business Problem A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maxim

KCP The official implementation of KCP: k Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching, accepted for p

This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement

What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical commu

extended_knapsack 0-1 knapsack with an additional constraint of maximum number of items used. Usage pip3 install extended_knapsack from extended_knaps

Most popular metrics used to evaluate object detection algorithms.

Realtime-YOLO-Monster-Detection-With-Non-Maximum-Supression Table of Contents In

Product-Review-Summarizer - Created a product review summarizer which clustered thousands of product reviews and summarized them into a maximum of 500 characters, saving precious time of customers and helping them make a wise buying decision.

crawler-banimode create crawler and get some new products with maximum discount in banimode website. این پروژه کوچک جهت یادگیری و کار با ابزار سلنیوم

xMCA | Maximum Covariance Analysis in Python The aim of this package is to provide a flexible tool for the climate science community to perform Maximu

Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re

Snakemake workflow: name A Snakemake workflow for description Usage The usage of this workflow is described in the Snakemake Workflow Catalog. If

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o

CMSF Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning" Requirements Python = 3.7.6 PyTorch

Mood bot This bot is created to help monitor your mood. Lean and mean. Deployment Install Docker and Docker Compose Populate .env file cp .env.dist .e

VeveBot You can follow me here Github | Twitter Features: - Click on the purchase at the time of the drop. - Be able to choose to do more than one tes

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

SMU_pytorch A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/ab

Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is

zEpid zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The purpose of this library is

Code for the paper "Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy" (arXiv:1611.04488; published at ICLR 2017), by Douga

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

tf-imle Tensorflow 2 and PyTorch implementation and Jupyter notebooks for Implicit Maximum Likelihood Estimation (I-MLE) proposed in the NeurIPS 2021

CRF - Conditional Random Fields A library for dense conditional random fields (CRFs). This is the official accompanying code for the paper Regularized

Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.

V-MPO Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pyt

(Subspace Constrained) Mean Shift Algorithms in Euclidean and/or Directional Product Spaces This repository contains Python3 code for the mean shift a

PyPi Conda The Multi-Mission Maximum Likelihood framework (3ML) A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.

Unet network with mean teacher for altrasound image segmentation

Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th

grungegirl. cli-based drug search for girls. welcome. grungegirl is aiming to be the premier drug culture application. it is the hacker's encyclopedia

Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte

Fast MST Algorithm Implementation of fast algorithms for (Maximum Spanning Tree) MST parsing that includes fast ArcMax+Reweighting+Tarjan algorithm fo

Pytorch-MBNet A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK Training To train a new model, please ru

CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

pyprobables is a pure-python library for probabilistic data structures. The goal is to provide the developer with a pure-python implementation of common probabilistic data-structures to use in their work.

Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte

Efficient implementations of Product Quantization and its variants using Pytorch and CUDA

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

CDIoU-CDIoUloss CDIoU and CDIoU loss is like a convenient plug-in that can be used in multiple models. CDIoU and CDIoU loss have different excellent p

Scene text recognition A real-time scene text recognition algorithm. Our system is able to recognize text in unconstrain background. This algorithm is

Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte