On the public citation graph

Ante-Hoc Methods for Interpretable Deep Models: A Survey

0 signed · 116 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ail2.61openalex-snapshot
DARPA 's explainable AI ( XAI ) program: A retrospective
inferredcites →https://doi.org/10.1002/widm.1391openalex-snapshot
A historical perspective of explainable Artificial Intelligence
inferredcites →https://doi.org/10.1002/wsbm.1548openalex-snapshot
Explainable deep learning in healthcare: A methodological survey from an attribution view
inferredcites →https://doi.org/10.1007/978-3-030-32236-6_51openalex-snapshot
Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges
inferredcites →https://doi.org/10.1007/978-3-030-64949-4_8openalex-snapshot
Survey of Explainable Machine Learning with Visual and Granular Methods Beyond Quasi-Explanations
inferredcites →https://doi.org/10.1007/978-3-030-70665-4_54openalex-snapshot
Interpretability of Deep Learning: A Survey
inferredcites →https://doi.org/10.1007/978-3-030-93736-2_34openalex-snapshot
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
inferredcites →https://doi.org/10.1007/978-3-031-63787-2_2openalex-snapshot
Prototype-Based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
inferredcites →https://doi.org/10.1007/978-3-031-73016-0_8openalex-snapshot
Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts
inferredcites →https://doi.org/10.1007/978-981-97-2259-4_5openalex-snapshot
Interpreting Pretrained Language Models via Concept Bottlenecks
inferredcites →https://doi.org/10.1007/bf00116251openalex-snapshot
Induction of Decision Trees
inferredcites →https://doi.org/10.1007/s00530-022-00960-4openalex-snapshot
A survey on the interpretability of deep learning in medical diagnosis
inferredcites →https://doi.org/10.1007/s10115-022-01756-8openalex-snapshot
Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond
inferredcites →https://doi.org/10.1007/s11263-019-01228-7openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1007/s11263-022-01657-xopenalex-snapshot
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50023-2openalex-snapshot
Fast Effective Rule Induction
inferredcites →https://doi.org/10.1016/j.artint.2018.07.007openalex-snapshot
Explanation in artificial intelligence: Insights from the social sciences
inferredcites →https://doi.org/10.1016/j.cag.2021.09.002openalex-snapshot
A survey of visual analytics for Explainable Artificial Intelligence methods
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106668openalex-snapshot
Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
inferredcites →https://doi.org/10.1016/j.ins.2022.10.013openalex-snapshot
Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey
inferredcites →https://doi.org/10.1016/j.knosys.2023.110273openalex-snapshot
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
inferredcites →https://doi.org/10.1016/j.media.2022.102470openalex-snapshot
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
inferredcites →https://doi.org/10.1016/j.neucom.2020.08.011openalex-snapshot
Explaining the black-box model: A survey of local interpretation methods for deep neural networks
inferredcites →https://doi.org/10.1016/j.patrec.2021.06.030openalex-snapshot
Perturbation-based methods for explaining deep neural networks: A survey
inferredcites →https://doi.org/10.1017/s1358246100005130openalex-snapshot
Contrastive Explanation
inferredcites →https://doi.org/10.1023/a:1022631118932openalex-snapshot
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
inferredcites →https://doi.org/10.1038/nature24270openalex-snapshot
Mastering the game of Go without human knowledge
inferredcites →https://doi.org/10.1038/s41467-024-47954-6openalex-snapshot
Identifying regions of importance in wall-bounded turbulence through explainable deep learning
inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-snapshot
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
inferredcites →https://doi.org/10.1038/s42256-020-00265-zopenalex-snapshot
Concept whitening for interpretable image recognition
inferredcites →https://doi.org/10.1049/ipr2.13074openalex-snapshot
Interpretable vision transformer based on prototype parts for COVID‐19 detection
inferredcites →https://doi.org/10.1080/01621459.1953.10483494openalex-snapshot
A Statistically Precise and Relatively Simple Method of Estimating the Bio-Assay with Quantal Response, Based on the Logistic Function
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2020.3032756openalex-snapshot
Review Study of Interpretation Methods for Future Interpretable Machine Learning
inferredcites →https://doi.org/10.1109/access.2021.3064838openalex-snapshot
These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition
inferredcites →https://doi.org/10.1109/access.2022.3204051openalex-snapshot
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
inferredcites →https://doi.org/10.1109/cvpr.2009.5206772openalex-snapshot
Describing objects by their attributes
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00075openalex-snapshot
Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01004openalex-snapshot
A Framework for Learning Ante-hoc Explainable Models via Concepts
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01008openalex-snapshot
B-cos Networks: Alignment is All We Need for Interpretability
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01042openalex-snapshot
High-Resolution Image Synthesis with Latent Diffusion Models
inferredcites →https://doi.org/10.1109/cvprw63382.2024.00512openalex-snapshot
Prototype-based Interpretable Model for Glaucoma Detection
inferredcites →https://doi.org/10.1109/dsaa.2018.00018openalex-snapshot
Explaining Explanations: An Overview of Interpretability of Machine Learning
inferredcites →https://doi.org/10.1109/iccv48922.2021.00108openalex-snapshot
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
inferredcites →https://doi.org/10.1109/ijcnn54540.2023.10191863openalex-snapshot
HQProtoPNet: An Evidence-Based Model for Interpretable Image Recognition
inferredcites →https://doi.org/10.1109/tetci.2021.3100641openalex-snapshot
A Survey on Neural Network Interpretability
inferredcites →https://doi.org/10.1109/tii.2022.3146552openalex-snapshot
From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where
inferredcites →https://doi.org/10.1109/tip.2024.3459626openalex-snapshot
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation
inferredcites →https://doi.org/10.1109/tkde.2020.2983930openalex-snapshot
A Survey of Data-driven and Knowledge-aware eXplainable AI
inferredcites →https://doi.org/10.1109/tnnls.2020.3027314openalex-snapshot
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
inferredcites →https://doi.org/10.1109/tpami.2013.140openalex-snapshot
Attribute-Based Classification for Zero-Shot Visual Object Categorization
inferredcites →https://doi.org/10.1109/trpms.2021.3066428openalex-snapshot
On Interpretability of Artificial Neural Networks: A Survey
inferredcites →https://doi.org/10.1109/uic-atc.2017.8397411openalex-snapshot
Interpretability of deep learning models: A survey of results
inferredcites →https://doi.org/10.1109/wacv57701.2024.00481openalex-snapshot
Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace
inferredcites →https://doi.org/10.1109/wacv57701.2024.00772openalex-snapshot
Prototype Learning for Explainable Brain Age Prediction
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/2339530.2339556openalex-snapshot
Intelligible models for classification and regression
inferredcites →https://doi.org/10.1145/2783258.2788613openalex-snapshot
Intelligible Models for HealthCare
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/3233231openalex-snapshot
The Mythos of Model Interpretability
inferredcites →https://doi.org/10.1145/3529755openalex-snapshot
On the Explainability of Natural Language Processing Deep Models
inferredcites →https://doi.org/10.1145/3645103openalex-snapshot
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
inferredcites →https://doi.org/10.1145/3674837openalex-snapshot
Decoupling Deep Learning for Enhanced Image Recognition Interpretability
inferredcites →https://doi.org/10.1145/3705724openalex-snapshot
Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges
inferredcites →https://doi.org/10.1177/1473871620904671openalex-snapshot
A survey of surveys on the use of visualization for interpreting machine learning models
inferredcites →https://doi.org/10.1214/15-aoas848openalex-snapshot
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
inferredcites →https://doi.org/10.1214/21-ss133openalex-snapshot
Interpretable machine learning: Fundamental principles and 10 grand challenges
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.1609/aaai.v32i1.11771openalex-snapshot
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions
inferredcites →https://doi.org/10.1609/aaai.v36i6.20551openalex-snapshot
Entropy-Based Logic Explanations of Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v36i8.20898openalex-snapshot
ProtGNN: Towards Self-Explaining Graph Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v37i9.26267openalex-snapshot
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis
inferredcites →https://doi.org/10.1609/aaai.v38i19.30109openalex-snapshot
On the Concept Trustworthiness in Concept Bottleneck Models
inferredcites →https://doi.org/10.1609/aimag.v38i3.2741openalex-snapshot
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
inferredcites →https://doi.org/10.1631/fitee.1700808openalex-snapshot
Visual interpretability for deep learning: a survey
inferredcites →https://doi.org/10.17863/cam.57507openalex-snapshot
Now You See Me (CME): Concept-based Model Extraction
inferredcites →https://doi.org/10.18653/v1/p19-1282openalex-snapshot
Is Attention Interpretable?
inferredcites →https://doi.org/10.23919/mipro.2018.8400040openalex-snapshot
Explainable artificial intelligence: A survey
inferredcites →https://doi.org/10.3390/e23010018openalex-snapshot
Explainable AI: A Review of Machine Learning Interpretability Methods
inferredcites →https://doi.org/10.3390/make3040048openalex-snapshot
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
inferredcites →https://doi.org/10.48550/arxiv.2006.15055openalex-snapshot
Object-Centric Learning with Slot Attention
inferredcites →https://doi.org/10.48550/arxiv.2311.14542openalex-snapshot
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge
inferredcites →https://doi.org/10.52202/075280-3353openalex-snapshot
Interpretable Prototype-based Graph Information Bottleneck

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/chem.202503666openalex-snapshot
Artificial Intelligence‐Guided Design of Fluorescent Probes for Biomedical Applications
inferred← citeshttps://doi.org/10.1007/s00170-026-19034-0openalex-snapshot
Open source deep learning models for accurate fastener defect inspection
inferred← citeshttps://doi.org/10.1007/s10489-026-07278-yopenalex-snapshot
An explainable deep learning method based on spectral co-clustering for ozone time series forecasting
inferred← citeshttps://doi.org/10.1007/s11187-026-01267-8openalex-snapshot
Adolescents’ entrepreneurial or employment intention for individualistic and communal business purposes
inferred← citeshttps://doi.org/10.1016/j.aei.2026.105301openalex-snapshot
LLM-driven personalized garment synthesis enhanced by neuro-symbolic RAG and physics-aware pattern optimization
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111092openalex-snapshot
KneeLigNet: a deep learning framework with multi-scale attention and context-aware refinement for accurate medial and lateral collateral ligament segmentation in knee MRI
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.111555openalex-snapshot
Dual-path continual learning with cross-scale attention for sparse physiological signal analysis
inferred← citeshttps://doi.org/10.1016/j.cie.2026.111805openalex-snapshot
Towards trustworthy AI in Industry 5.0: Ante-hoc interpretability with deep learning
inferred← citeshttps://doi.org/10.1016/j.ifacol.2025.12.532openalex-snapshot
Graph-Attentive Kolmogorov-Arnold Networks for Interpretable EEG Emotion Recognition
inferred← citeshttps://doi.org/10.1016/j.jer.2026.09.035openalex-snapshot
Creating digital twins and shadows of existing electro-hydraulic machinery in the steel industry
inferred← citeshttps://doi.org/10.1016/j.jii.2026.101117openalex-snapshot
Information integration for Industry 5.0: A review of IIoT–edge–cloud architectures with 6G/ISAC capabilities
inferred← citeshttps://doi.org/10.1016/j.jii.2026.101192openalex-snapshot
Human-Centric, AI-Enabled Concurrent Engineering: Towards a cross-domain framework
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.117034openalex-snapshot
Knowledge-guided federated intelligence for explainable and privacy-aware multimodal decision systems
inferred← citeshttps://doi.org/10.1016/j.patter.2026.101534openalex-snapshot
Pitfalls and risks of generative AI in machine learning
inferred← citeshttps://doi.org/10.1016/j.procs.2025.10.070openalex-snapshot
Interpretable vs. Post-Hoc Explained Models for Digital Twin Applications in Clinical Forecasting: A Multiple Sclerosis Case Study
inferred← citeshttps://doi.org/10.1016/j.rser.2026.117462openalex-snapshot
AI based underwater ocean waste detection and classification: a comprehensive review, key issues and future direction
inferred← citeshttps://doi.org/10.1016/j.tifs.2025.105333openalex-snapshot
Artificial intelligence for flavor perception: Integrating olfactory mechanisms into food group sensory evaluation
inferred← citeshttps://doi.org/10.1093/bib/bbag564openalex-snapshot
Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions
inferred← citeshttps://doi.org/10.1109/access.2026.3663161openalex-snapshot
Explainable Artificial Intelligence (XAI): Concepts, Applications, Challenges, and Future Perspectives
inferred← citeshttps://doi.org/10.1109/bigdata66926.2025.11400757openalex-snapshot
Explaining Clinical Outcome Predictions with Compositional Rationale Extraction Model
inferred← citeshttps://doi.org/10.1109/ccgridw69005.2026.00055openalex-snapshot
Acoustic Features as Potential Vocal Biomarkers for Multiple Sclerosis
inferred← citeshttps://doi.org/10.1109/iscc65549.2025.11326468openalex-snapshot
Enhancing AI Explainability and Performance in Pulmonary Condition Classification with Data Segmentation and Augmentation
inferred← citeshttps://doi.org/10.1145/3770855.3817786openalex-snapshot
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
inferred← citeshttps://doi.org/10.1145/3774904.3792986openalex-snapshot
Concept Relationship Embedding-Based Interactive Web Application for Explainable Medical Diagnosis
inferred← citeshttps://doi.org/10.1177/09544054261489838openalex-snapshot
Data-driven robust design for industrial cylindrical plunge grinding of piston rings: Requirements for reliable digital-twin development under production noise
inferred← citeshttps://doi.org/10.2139/ssrn.6923099openalex-snapshot
An interpretable safety assessment method for battery thermal runaway based on belief rule base
inferred← citeshttps://doi.org/10.54287/gujsa.1870409openalex-snapshot
Uncertainty-Gated Dual-Branch Additive–Attention Network for Robust and Calibrated Tabular Classification Under Missingness
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.