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Explainable Models

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Showing 51–100 of 128 papers

TitleStatusHype
AudioProtoPNet: An interpretable deep learning model for bird sound classification—0
An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion—0
Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and ChallengesCode0
Deep Learning for Robust and Explainable Models in Computer Vision—0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
Not just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition—0
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations—0
Gaussian Process Neural Additive ModelsCode0
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks—0
Can Physician Judgment Enhance Model Trustworthiness? A Case Study on Predicting Pathological Lymph Nodes in Rectal Cancer—0
Prototypical Self-Explainable Models Without Re-trainingCode0
A Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis—0
Stable and Interpretable Deep Learning for Tabular Data: Introducing InterpreTabNet with the Novel InterpreStability Metric—0
Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice—0
Hyperbolic Convolutional Neural Networks—0
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities—0
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology—0
Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy—0
Towards eXplainable AI for Mobility Data Science—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
Interpreting Deep Forest through Feature Contribution and MDI Feature Importance—0
GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets—0
The State of Human-centered NLP Technology for Fact-checking—0
Multimodal and Explainable Internet Meme Classification—0
Prototype-based Interpretable Graph Neural NetworksCode0
Towards Human-centered Explainable AI: A Survey of User Studies for Model ExplanationsCode0
The Ability of Image-Language Explainable Models to Resemble Domain Expertise—0
Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
The Analysis of Synonymy and Antonymy in Discourse Relations: An interpretable Modeling Approach—0
Smoothing Entailment Graphs with Language ModelsCode0
Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology—0
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches—0
Explainable Models via Compression of Tree Ensembles—0
Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models—0
ConceptDistil: Model-Agnostic Distillation of Concept Explanations—0
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers—0
Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting—0
Nuclei panoptic segmentation and composition regression with multi-task deep neural networks—0
CoNIC: Colon Nuclei Identification and Counting Challenge 2022—0
A deep convolutional neural network for classification of Aedes albopictus mosquitoes—0
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems—0
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes—0
Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation—0
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction—0
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop—0
Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 dataCode0
Deriving Explanation of Deep Visual Saliency Models—0
A Framework for Learning Ante-hoc Explainable Models via ConceptsCode0
Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks—0
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