Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA
Question Answering (QA) systems are increasingly deployed in applications where they support real-world decisions. However, state-of-the-art models rely on deep neural networks, which are difficult to interpret by humans…
Adversarial AttackExplainable ModelsKnowledge GraphsPrediction+1Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax
We show that both an LSTM and a unitary-evolution recurrent neural network (URN) can achieve encouraging accuracy on two types of syntactic patterns: context-free long distance agreement, and mildly context-sensitive cro…
Explainable ModelsWord EmbeddingsThe Analysis of Synonymy and Antonymy in Discourse Relations: An interpretable Modeling Approach
The idea that discourse relations are construed through explicit content and shared, or implicit, knowledge between producer and interpreter is ubiquitous in discourse research and linguistics. However, the actual contri…
Explainable ModelsSmoothing Entailment Graphs with Language Models
The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by Open Relation Extraction (ORE). EGs are computationally efficient and expla…
DiversityExplainable ModelsLanguage ModelingLanguage Modelling+2Using Multi-modal Data for Improving Generalizability and Explainability of Disease Classification in Radiology
Traditional datasets for the radiological diagnosis tend to only provide the radiology image alongside the radiology report. However, radiology reading as performed by radiologists is a complex process, and information s…
Explainable ModelsdCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led the algorithm to some decisions is a rea…
ClassificationExplainable ModelsTime SeriesTime Series AnalysisExplaining Chest X-ray Pathologies in Natural Language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to con…
Explainable ModelsMachine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches
Machine Learning has become an integral part of engineering design and decision making in several domains, including sports. Deep Neural Networks (DNNs) have been the state-of-the-art methods for predicting outcomes of p…
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Explainable Models+1Explainable Models via Compression of Tree Ensembles
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models (PLMs). While effective, they lose one o…
Explainable ModelsVisualization of Decision Trees based on General Line Coordinates to Support Explainable Models
Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Deci…
Explainable ModelsConceptDistil: Model-Agnostic Distillation of Concept Explanations
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, im…
Explainable ModelsKnowledge DistillationmodelMulti-Task LearningPost Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommende…
DiversityExplainable ModelsExplainable RecommendationInformation Retrieva+7Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and tre…
AnatomyDeep LearningExplainable ModelsGenerative Adversarial Network+4UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require different model architectures (e.g., ge…
Explainable ModelsInformation RetrievalQuestion AnsweringRetrievalSeparable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…
ClassificationExplainable ModelsNuclear SegmentationSegmentationOne Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification
The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue …
AllCell DetectionExplainable ModelsImage Segmentation+4Nuclei panoptic segmentation and composition regression with multi-task deep neural networks
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…
DecoderExplainable ModelsMulti-Task LearningNuclear Segmentation+3SLISEMAP: Supervised dimensionality reduction through local explanations
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but the…
ClassificationDimensionality ReductionExplainable ModelsGPU+1CoNIC: Colon Nuclei Identification and Counting Challenge 2022
Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…
Explainable ModelsNuclear SegmentationA deep convolutional neural network for classification of Aedes albopictus mosquitoes
Monitoring the spread of disease-carrying mosquitoes is a first and necessary step to control severe diseases such as dengue, chikungunya, Zika or yellow fever. Previous citizen science projects have been able to obtain …
ClassificationExplainable ModelsTransfer Learning