Interpretable Machine Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 226 papers with code (537 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
  7 Oct 2016 126 repositories listed Syntology ran 79 of 141 samples · 62 unverified · 68 pointer-only (licence)
  For captioning and VQA, we show that even non-attention based models can localize inputs.
• Axiomatic Attribution for Deep Networks
  4 Mar 2017 40 repositories listed Syntology ran 36 of 56 samples · 20 unverified · 17 pointer-only (licence)
  We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works.
• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
  19 Dec 2019 36 repositories listed Syntology ran 5 of 10 samples · 5 unverified · 5 pointer-only (licence)
  Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier
  16 Feb 2016 27 repositories listed Syntology ran 5 of 19 samples · 14 unverified
  Despite widespread adoption, machine learning models remain mostly black boxes.
• SmoothGrad: removing noise by adding noise
  12 Jun 2017 20 repositories listed
  Explaining the output of a deep network remains a challenge.
• A Unified Approach to Interpreting Model Predictions
  22 May 2017 17 repositories listed Syntology ran 3 of 8 samples · 5 unverified · 6 pointer-only (licence)
  Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.
• Learning Important Features Through Propagating Activation Differences
  10 Apr 2017 14 repositories listed Syntology ran 8 of 15 samples · 7 unverified · 2 pointer-only (licence)
  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…
• RISE: Randomized Input Sampling for Explanation of Black-box Models
  19 Jun 2018 13 repositories listed Syntology ran 1 of 34 samples · 33 unverified · 10 pointer-only (licence)
  We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments.
• Interpretable Explanations of Black Boxes by Meaningful Perturbation
  11 Apr 2017 13 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at…
• Neural Additive Models: Interpretable Machine Learning with Neural Nets
  29 Apr 2020 8 repositories listed Syntology ran 26 of 33 samples · 7 unverified · 14 pointer-only (licence)
  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.
• BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
  7 Apr 2020 8 repositories listed
  This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
• Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
  30 May 2021 7 repositories listed
  In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM.
• Understanding Neural Networks Through Deep Visualization
  22 Jun 2015 7 repositories listed Syntology ran 0 of 5 samples · 5 unverified · 2 pointer-only (licence)
  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.
• Interpretable machine learning: definitions, methods, and applications
  14 Jan 2019 6 repositories listed Syntology ran 2 of 27 samples · 25 unverified
  Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv.
• Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
  2 May 2023 4 repositories listed Syntology ran 2 of 29 samples · 27 unverified
  PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning…
• Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees
  18 May 2019 4 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains.
• ProtoAttend: Attention-Based Prototypical Learning
  17 Feb 2019 4 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 1 pointer-only (licence)
  We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes.
• Interpretable machine learning for time-to-event prediction in medicine and healthcare
  17 Mar 2023 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Time-to-event prediction, e.
• Fast Sparse Decision Tree Optimization via Reference Ensembles
  1 Dec 2021 3 repositories listed
  We show that by using these guesses, we can reduce the run time by multiple orders of magnitude, while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power.
• Relative Feature Importance
  16 Jul 2020 3 repositories listed
  Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model.
• Explaining Groups of Points in Low-Dimensional Representations
  3 Mar 2020 3 repositories listed
  A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they…
• MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
  27 Sep 2019 3 repositories listed
  With a mortality rate of 5.
• Improving performance of deep learning models with axiomatic attribution priors and expected gradients
  25 Jun 2019 3 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable…
• Explaining a black-box using Deep Variational Information Bottleneck Approach
  19 Feb 2019 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.
• Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  26 Nov 2018 3 repositories listed
  Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains.
• Neurosymbolic Association Rule Mining from Tabular Data
  27 Apr 2025 2 repositories listed
  Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains.
• LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
  29 Apr 2024 2 repositories listed
  Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines.
• Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
  4 Apr 2024 2 repositories listed
  We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs).
• TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
  21 Dec 2023 2 repositories listed
  We introduce TraceFL, a fine-grained neuron provenance capturing mechanism that identifies clients responsible for a global model's prediction by tracking the flow of information from individual clients to the global…
• Take 5: Interpretable Image Classification with a Handful of Features
  23 Mar 2023 2 repositories listed
  We argue that a human can only understand the decision of a machine learning model, if the features are interpretable and only very few of them are used for a single decision.

Syntology lines on 17 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.