Python Shannon-entropy Resources

An entropy-based strategy to wordle

Implementation for "Conditional entropy minimization principle for learning domain invariant representation features". The code is reproduced from thi

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

WordleSolver uses Entropy to find the best next guess for Wordle, given the color clues use player.py and enter in the string for the suggested clue w

Sentiment Classification using WSD, Maximum Entropy & Naive Bayes Classifiers

Hashgen A simple Python script for generating a variety of hashes from safe urandom entropy. For whenever you need a random hash (e.g. generating an a

This repository is no longer maintained.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

Code For the Paper "Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective" Author: Robert Bamler Date: 22 D

truffleHog Searches through git repositories for secrets, digging deep into commit history and branches. This is effective at finding secrets accident

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

Fine-grainedImageClassification Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification We trained model here: lin

A way to analyse how malware and/or goodware samples vary from each other using Shannon Entropy, Hausdorff Distance and Jaro-Winkler Distance

Enhancing Twin Delayed Deep Deterministic Policy Gradient with Cross-Entropy Method Hieu Trung Nguyen, Khang Tran and Ngoc Hoang Luong Setup Clone thi

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

code for "Improving GAN Training via Binarized Representation Entropy (BRE) Regularization" (ICLR2018 paper) paper: https://arxiv.org/abs/1805.03644 G

Wikipedia Extractive Text Summarizer + Keywords Identification (entropy-based)Wikipedia Extractive Text Summarizer + Keywords Identification (entropy-based)

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

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

Collaborative-driven Quantum Feature Selection This repository was developed by Riccardo Nembrini, PhD student at Politecnico di Milano. See the websi

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

Semi-supervised Domain Adaptation via Minimax Entropy (ICCV 2019) Install pip install -r requirements.txt The code is written for Pytorch 0.4.0, but s

Python module ordered ordered module is the opposite to random - it maintains order in the program. import random x = 5 def increase(): global x

Deep Unsupervised Image Hashing by Maximizing Bit Entropy This is the PyTorch implementation of accepted AAAI 2021 paper: Deep Unsupervised Image Hash

State Entropy Maximization with Random Encoders for Efficient Exploration (RE3) (ICML 2021) Code for State Entropy Maximization with Random Encoders f

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

⛺️ Tent: Fully Test-Time Adaptation by Entropy Minimization This is the official project repository for Tent: Fully-Test Time Adaptation by Entropy Mi

PyTorch Code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation Viraj Prabhu, Shivam Khare, Deeks

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e