neuro-symbolic-sudoku-solver VS DiCE

Compare neuro-symbolic-sudoku-solver vs DiCE and see what are their differences.

neuro-symbolic-sudoku-solver

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations. (by ashutosh1919)
neurosymboliccomputing Machine Learning Deep Learning reinforcement-learning neural-network neural-logic-machines sudoku-solver sudoku Artificial intelligence artificial-neural-networks reinforcement-learning-agent explainable-ai solving-sudoku-puzzles
Source Code
arxiv.org
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DiCE

Generate Diverse Counterfactual Explanations for any machine learning model. (by interpretml)
counterfactual-explanations explainable-ml xai interpretable-machine-learning Deep Learning Machine Learning explainable-ai
Source Code
interpretml.github.io
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neuro-symbolic-sudoku-solver DiCE
1 2
66 1,270
- 0.9%
0.0 8.2
over 2 years ago 14 days ago
Python Python
Apache License 2.0 MIT License
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neuro-symbolic-sudoku-solver

Posts with mentions or reviews of neuro-symbolic-sudoku-solver. We have used some of these posts to build our list of alternatives and similar projects.

DiCE

Posts with mentions or reviews of DiCE. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-31.
  • [D] Have researchers given up on traditional machine learning methods?
    2 projects | /r/MachineLearning | 31 Jan 2023
    - all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
  • [R] The Shapley Value in Machine Learning
    1 project | /r/MachineLearning | 25 Feb 2022
    Counter-factual and recourse-based explanations are alternative approach to model explanations. I used to work in a large financial institution, and we were researching whether counter-factual explanation methods would lead to better reason codes for adverse action notices.

What are some alternatives?

When comparing neuro-symbolic-sudoku-solver and DiCE you can also consider the following projects:

transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

OmniXAI - OmniXAI: A Library for eXplainable AI

MAGIST-Algorithm - Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Zeta

CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

sudoku - Can Neural Networks Crack Sudoku?

AIX360 - Interpretability and explainability of data and machine learning models

DragGAN - Unofficial implementation of the DragGAN paper

interpret - Fit interpretable models. Explain blackbox machine learning.

TicTacToe - Tic Tac Toe game, designed to be used to train a Deep Neural Network via Reinforcement Learning (DQN). It can also be played by 2 humans and features a hard coded AI that never looses and will win if you do not do perfect play against it.

harakiri - Help applications kill themselves

Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

stranger - Chat anonymously with a randomly chosen stranger

neuro-symbolic-sudoku-solver vs transformers-interpret DiCE vs OmniXAI neuro-symbolic-sudoku-solver vs MAGIST-Algorithm DiCE vs CARLA neuro-symbolic-sudoku-solver vs sudoku DiCE vs AIX360 neuro-symbolic-sudoku-solver vs DragGAN DiCE vs interpret neuro-symbolic-sudoku-solver vs TicTacToe DiCE vs harakiri neuro-symbolic-sudoku-solver vs Agar.io_Q-Learning_AI DiCE vs stranger