Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
Benchmarking the Attribution Quality of Vision Models
Attribution maps are one of the most established tools to explain the functioning of computer vision models. They assign importance scores to input features, indicating how relevant each feature is for the prediction of …
BenchmarkingExplainable ModelsPredefined Prototypes for Intra-Class Separation and Disentanglement
Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in scenarios with little labeled data or to …
DisentanglementExplainable ModelsExplainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions
This work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bittern…
Drug DesignExplainable ModelsFreqRISE: Explaining time series using frequency masking
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision making. To develop explainable artificia…
Decision MakingExplainable artificial intelligenceExplainable ModelsTime SeriesProtoS-ViT: Visual foundation models for sparse self-explainable classifications
Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and associate with a certain object or entit…
Explainable ModelsAccurate Explanation Model for Image Classifiers using Class Association Embedding
Image classification is a primary task in data analysis where explainable models are crucially demanded in various applications. Although amounts of methods have been proposed to obtain explainable knowledge from the bla…
ClassificationExplainable Modelsimage-classificationImage Classification+1Data Science Principles for Interpretable and Explainable AI
Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible softw…
Explainable ModelsLarge Language Models for Cyber Security: A Systematic Literature Review
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats …
Explainable ModelsIntrusion DetectionMalware AnalysisNetwork Intrusion Detection+3Stability of Explainable Recommendation
Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reas…
Explainable ModelsExplainable RecommendationRecommendation SystemsAudioProtoPNet: An interpretable deep learning model for bird sound classification
Deep learning models have significantly advanced acoustic bird monitoring by being able to recognize numerous bird species based on their vocalizations. However, traditional deep learning models are black boxes that prov…
Audio ClassificationClassificationDecision MakingDeep Learning+2An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying in…
Decision MakingExplainable ModelsFairnessPrototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainable models, like prototype-based models, c…
Explainable ModelsDeep Learning for Robust and Explainable Models in Computer Vision
Recent breakthroughs in machine and deep learning (ML and DL) research have provided excellent tools for leveraging enormous amounts of data and optimizing huge models with millions of parameters to obtain accurate netwo…
Adversarial AttackDeep LearningExplainable ModelsPantypes: Diverse Representatives for Self-Explainable Models
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in their decisions by basing inference on si…
DiversityExplainable ModelsFairnessNot just Birds and Cars: Generic, Scalable and Explainable Models for Professional Visual Recognition
Some visual recognition tasks are more challenging then the general ones as they require professional categories of images. The previous efforts, like fine-grained vision classification, primarily introduced models tailo…
Explainable Modelsobject-detectionObject DetectionQUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
Deep Neural Networks (DNNs) stand out as one of the most prominent approaches within the Machine Learning (ML) domain. The efficacy of DNNs has surged alongside recent increases in computational capacity, allowing these …
counterfactualExplainable ModelsGaussian Process Neural Additive Models
Deep neural networks have revolutionized many fields, but their black-box nature also occasionally prevents their wider adoption in fields such as healthcare and finance, where interpretable and explainable models are re…
Additive modelsExplainable ModelsAdvancing Ante-Hoc Explainable Models through Generative Adversarial Networks
This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks. Our approach appends an unsupervised explanation generator to the primary classi…
Explainable ModelsTraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborative yet privacy-preserving training comes…
Explainable ModelsFault localizationFederated Learningimage-classification+6Can Physician Judgment Enhance Model Trustworthiness? A Case Study on Predicting Pathological Lymph Nodes in Rectal Cancer
Explainability is key to enhancing artificial intelligence's trustworthiness in medicine. However, several issues remain concerning the actual benefit of explainable models for clinical decision-making. Firstly, there is…
Decision MakingExplainable Models