On the public citation graph

The Decoupling Concept Bottleneck Model

https://doi.org/10.1109/tpami.2024.3489597
0 signed · 51 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-3-030-01237-3_8openalex-snapshot
Interpretable Basis Decomposition for Visual Explanation
inferredcites →https://doi.org/10.1007/978-3-031-72980-5_27openalex-snapshot
Online Zero-Shot Classification with CLIP
inferredcites →https://doi.org/10.1007/s11263-021-01498-0openalex-snapshot
Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations
inferredcites →https://doi.org/10.1007/s11747-019-00710-5openalex-snapshot
Explainable AI: from black box to glass box
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1038/s41586-022-05172-4openalex-snapshot
Discovering faster matrix multiplication algorithms with reinforcement learning
inferredcites →https://doi.org/10.1038/s41586-023-06185-3openalex-snapshot
Accurate medium-range global weather forecasting with 3D neural networks
inferredcites →https://doi.org/10.1038/s42003-021-02393-7openalex-snapshot
Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade
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/s42256-022-00483-7openalex-snapshot
An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors
inferredcites →https://doi.org/10.1063/5.0110342openalex-snapshot
DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows
inferredcites →https://doi.org/10.1109/72.392253openalex-snapshot
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
inferredcites →https://doi.org/10.1109/access.2022.3167702openalex-snapshot
Concept Bottleneck Model With Additional Unsupervised Concepts
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.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.354openalex-snapshot
Network Dissection: Quantifying Interpretability of Deep Visual Representations
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00261openalex-snapshot
Rethinking Semantic Segmentation: A Prototype View
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/cvpr52729.2023.01826openalex-snapshot
LANA: A Language-Capable Navigator for Instruction Following and Generation
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01839openalex-snapshot
Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification
inferredcites →https://doi.org/10.1109/iccv.2015.425openalex-snapshot
Deep Learning Face Attributes in the Wild
inferredcites →https://doi.org/10.1109/iccv51070.2023.00287openalex-snapshot
Learning Concise and Descriptive Attributes for Visual Recognition
inferredcites →https://doi.org/10.1109/iccvw60793.2023.00292openalex-snapshot
Sparse Linear Concept Discovery Models
inferredcites →https://doi.org/10.1109/jbhi.2018.2824327openalex-snapshot
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets
inferredcites →https://doi.org/10.1109/tfuzz.2022.3144448openalex-snapshot
Explainable CNN With Fuzzy Tree Regularization for Respiratory Sound Analysis
inferredcites →https://doi.org/10.1109/tpami.2017.2723009openalex-snapshot
Places: A 10 Million Image Database for Scene Recognition
inferredcites →https://doi.org/10.1109/tpami.2020.2982882openalex-snapshot
Interpretable CNNs for Object Classification
inferredcites →https://doi.org/10.1109/tpami.2021.3059968openalex-snapshot
Image Segmentation Using Deep Learning: A Survey
inferredcites →https://doi.org/10.1109/tpami.2021.3115452openalex-snapshot
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
inferredcites →https://doi.org/10.1109/tvcg.2022.3209435openalex-snapshot
Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing
inferredcites →https://doi.org/10.1109/wacv56688.2023.00476openalex-snapshot
Concept Correlation and Its Effects on Concept-Based Models
inferredcites →https://doi.org/10.1109/wacv57701.2024.00085openalex-snapshot
Interpretable Object Recognition by Semantic Prototype Analysis
inferredcites →https://doi.org/10.1146/annurev-biodatasci-092820-114757openalex-snapshot
Ethical Machine Learning in Healthcare
inferredcites →https://doi.org/10.1214/21-ss133openalex-snapshot
Interpretable machine learning: Fundamental principles and 10 grand challenges
inferredcites →https://doi.org/10.1609/aaai.v32i1.11501openalex-snapshot
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
inferredcites →https://doi.org/10.1609/aaai.v35i9.16934openalex-snapshot
Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability
inferredcites →https://doi.org/10.1609/aaai.v37i5.25736openalex-snapshot
Interactive Concept Bottleneck Models
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/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1806.07538openalex-snapshot
Towards Robust Interpretability with Self-Explaining Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2309.16928openalex-snapshot
Learning to Receive Help: Intervention-Aware Concept Embedding Models
inferredcites →https://doi.org/10.48550/arxiv.2401.14142openalex-snapshot
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
inferredcites →https://doi.org/10.57702/49zdxji5openalex-snapshot
CIFAR-10 and CIFAR-100 Datasets

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4835782openalex-extracted
Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Model
inferred← citeshttps://doi.org/10.1007/978-3-032-38079-1_10openalex-snapshot
ConceptIL: Concept Incremental Learning for Interpretable Medical AI
inferred← citeshttps://doi.org/10.1016/j.media.2026.103957openalex-snapshot
Channel-wise joint disentanglement representation learning for B-mode and super-resolution ultrasound based CAD of breast cancer
inferred← citeshttps://doi.org/10.1186/s43046-026-00397-0openalex-snapshot
From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research
inferred← citeshttps://doi.org/10.2139/ssrn.4820883openalex-snapshot
Enhancing Explainability Through Visual Concept Knowledge Distillation on Concept Bottleneck Model
inferred← citeshttps://doi.org/10.3390/app15020493openalex-snapshot
Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
inferred← citeshttps://doi.org/10.3390/app16199423openalex-snapshot
AI-Assisted Rehabilitation Assessment for Pediatric Stroke: A Methodological Framework with Explainable Speech Concept Modeling, Video-Based Motor Representation, and Age-Conditioned Normalization
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.