Interpretable Machine Learning

104 papers with code • 0 benchmarks • 2 datasets

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Greatest papers with code

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

google-research/google-research 19 Dec 2019

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.

Interpretable Machine Learning Time Series +1

ProtoAttend: Attention-Based Prototypical Learning

google-research/google-research 17 Feb 2019

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.

Decision Making General Classification +1

Neural Additive Models: Interpretable Machine Learning with Neural Nets

google-research/google-research NeurIPS 2021

They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.

Additive models Decision Making +1

SmoothGrad: removing noise by adding noise

slundberg/shap 12 Jun 2017

Explaining the output of a deep network remains a challenge.

Interpretable Machine Learning

A Unified Approach to Interpreting Model Predictions

slundberg/shap NeurIPS 2017

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.

Feature Importance Interpretable Machine Learning

Learning Important Features Through Propagating Activation Differences

slundberg/shap ICML 2017

Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input.

Interpretable Machine Learning

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

marcotcr/lime 16 Feb 2016

Despite widespread adoption, machine learning models remain mostly black boxes.

Image Classification Interpretable Machine Learning

How Interpretable and Trustworthy are GAMs?

microsoft/interpret 11 Jun 2020

Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.

Additive models Interpretable Machine Learning

Understanding Neural Networks Through Deep Visualization

yosinski/deep-visualization-toolbox 22 Jun 2015

The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e. g. a live webcam stream).

Interpretable Machine Learning

Full-Gradient Representation for Neural Network Visualization

jacobgil/pytorch-grad-cam NeurIPS 2019

Our experiments reveal that our method explains model behaviour correctly, and more comprehensively than other methods in the literature.

Interpretable Machine Learning