38 Repositories
Python maximum Libraries
Maximum Spatial Perturbation for Image-to-Image Translation (Official Implementation)
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
Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers
Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers
A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding
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
K Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching (To appear in RA-L 2022)
KCP The official implementation of KCP: k Closest Points and Maximum Clique Pruning for Efficient and Effective 3D Laser Scan Matching, accepted for p
Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.
This repository is no longer maintained. Please use our new Softlearning package instead. Soft Actor-Critic Soft actor-critic is a deep reinforcement
Survival analysis in Python
What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical commu
0-1 knapsack with an additional constraint of maximum number of items used
extended_knapsack 0-1 knapsack with an additional constraint of maximum number of items used. Usage pip3 install extended_knapsack from extended_knaps
Realtime YOLO Monster Detection With Non Maximum Supression
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.
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.
Create crawler get some new products with maximum discount in banimode website
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
Maximum Covariance Analysis in Python
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: Exact maximum likelihood with dimensionality reduction.
Funnels This repository contains the code needed to reproduce the experiments from the paper: Funnels: Exact maximum likelihood with dimensionality re
Snakemake workflow for converting FASTQ files to self-contained CRAM files with maximum lossless compression.
Snakemake workflow: name A Snakemake workflow for description Usage The usage of this workflow is described in the Snakemake Workflow Catalog. If
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"
Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o
A python bot that will allow you to have maximum luck during Veve drops.
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
A Pytorch implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE
SMU_pytorch A Pytorch Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/ab
Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
Softlearning Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. The implementation is
Epidemiology analysis package
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
Learning kernels to maximize the power of MMD tests
Code for the paper "Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy" (arXiv:1611.04488; published at ICLR 2017), by Douga
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE
SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition
Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation
tf-imle Tensorflow 2 and PyTorch implementation and Jupyter notebooks for Implicit Maximum Likelihood Estimation (I-MLE) proposed in the NeurIPS 2021
Predicting path with preference based on user demonstration using Maximum Entropy Deep Inverse Reinforcement Learning in a continuous environment
Preference-Planning-Deep-IRL Introduction Check my portfolio post Dependencies Gym stable-baselines3 PyTorch Usage Take Demonstration python3 record.
Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO)
V-MPO Simple code to demonstrate Deep Reinforcement Learning by using an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) in Pyt
The Multi-Mission Maximum Likelihood framework (3ML)
PyPi Conda The Multi-Mission Maximum Likelihood framework (3ML) A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models
Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th
grungegirl is the hacker's drug encyclopedia. programmed in python for maximum modularity and ease of configuration.
grungegirl. cli-based drug search for girls. welcome. grungegirl is aiming to be the premier drug culture application. it is the hacker's encyclopedia
No effort, no worry, maximum performance.
Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte
Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.
Fast MST Algorithm Implementation of fast algorithms for (Maximum Spanning Tree) MST parsing that includes fast ArcMax+Reweighting+Tarjan algorithm fo
graph-theoretic framework for robust pairwise data association
CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides
No effort, no worry, maximum performance.
Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte
TorchPQ is a python library for Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) on GPU using Product Quantization (PQ) algorithm.
Efficient implementations of Product Quantization and its variants using Pytorch and CUDA
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"
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
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo
CDIoU and CDIoU loss is like a convenient plug-in that can be used in multiple models. CDIoU and CDIoU loss have different excellent performances in several models such as Faster R-CNN, YOLOv4, RetinaNet and . There is a maximum AP improvement of 1.9% and an average AP of 0.8% improvement on MS COCO dataset, compared to traditional evaluation-feedback modules. Here we just use as an example to illustrate the code.
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 detection and recognition based on Extremal Region(ER)
Scene text recognition A real-time scene text recognition algorithm. Our system is able to recognize text in unconstrain background. This algorithm is
No effort, no worry, maximum performance.
Django Cachalot Caches your Django ORM queries and automatically invalidates them. Documentation: http://django-cachalot.readthedocs.io Table of Conte