117 Open source projects that are alternatives of or similar to Modelstudio

A library for finding knowledge neurons in pretrained transformer models.

[ECCV 2018] code for Choose Your Neuron: Incorporating Domain Knowledge Through Neuron Importance

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.

🏔️ Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations

For calculating global feature importance using Shapley values.

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Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Exploratory, Inferential and Predictive data analysis. Feel free to show your ❤️ by giving a star ⭐

BAS R package https://merliseclyde.github.io/BAS/

official implementation of "Visualization of Convolutional Neural Networks for Monocular Depth Estimation"

Multi-time-horizon solar forecasting using recurrent neural network

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Python package to visualize and cluster partial dependence.

Code for "Free-Lunch Saliency via Attention in Atari Agents"

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

Kaggle Kernels (Python, R, Jupyter Notebooks)

Concept Bottleneck Models, ICML 2020

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

R wrapper for MLJAR API

UFC bout winner prediction using neural nets.

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Molecular Explanation Generator

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Predict time-series with one line of code.

Data generator for Arena - interactive XAI dashboard

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

NeurIPS 2018. Linear-time model comparison tests.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Collection of NLP model explanations and accompanying analysis tools

Machine Learning Finite State Machine Models from Data with Genetic Algorithms

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM)

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Model interpretability and understanding for PyTorch

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

A collection of research materials on explainable AI/ML

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A Python package implementing a new machine learning model for text classification with visualization tools for Explainable AI

A curated list of awesome machine learning interpretability resources.

A game theoretic approach to explain the output of any machine learning model.

Surrogate Modeling Toolbox

Contrastive Predictive Coding for Automatic Speaker Verification

An open source book to learn data science, data analysis and machine learning, suitable for all ages!

Launch machine learning models into production using flask, docker etc.

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM