Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to di…
counterfactualExplainable ModelsExplainable RecommendationRecommendation SystemsPhysically Explainable CNN for SAR Image Classification
Integrating the special electromagnetic characteristics of Synthetic Aperture Radar (SAR) in deep neural networks is essential in order to enhance the explainability and physics awareness of deep learning. In this paper,…
ClassificationExplainable Modelsimage-classificationImage ClassificationDeconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes
In vision-language retrieval systems, users provide natural language feedback to find target images. Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval. …
Explainable ModelsLanguage ModellingRetrievalThink Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generation
Implicit knowledge, such as common sense, is key to fluid human conversations. Current neural response generation (RG) models are trained to generate responses directly, omitting unstated implicit knowledge. In this pape…
Common Sense ReasoningExplainable ModelsResponse GenerationSelf-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers. E…
Deep LearningExplainable ModelsICU MortalityMortality PredictionConsistent Explanations by Contrastive Learning
Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human p…
Contrastive LearningExplainable artificial intelligenceExplainable ModelsSelf-Supervised LearningFaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the information (support or refute the claim) b…
Explainable ModelsFact CheckingGlobal and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data
The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors such as the severity of the disease, infec…
BIG-bench Machine LearningExplainable Modelsseverity predictionDeriving Explanation of Deep Visual Saliency Models
Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding h…
Explainable ModelsSaliency PredictionA Framework for Learning Ante-hoc Explainable Models via Concepts
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we prop…
Explainable ModelsExplanation GenerationDistinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atro…
counterfactualExplainable ModelsMixture of Linear Models Co-supervised by Deep Neural Networks
Deep neural network (DNN) models have achieved phenomenal success for applications in many domains, ranging from academic research in science and engineering to industry and business. The modeling power of DNN is believe…
Decision MakingExplainable ModelsUAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via Translations
We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word corre…
Explainable ModelsTask 2TranslationWhen and How to Fool Explainable Models (and Humans) with Adversarial Examples
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness aga…
BIG-bench Machine LearningExplainable ModelsAn XAI Approach to Deep Learning Models in the Detection of DCIS
The results showed that XAI could indeed be used as a proof of concept to begin discussions on the implementation of assistive AI systems within the clinical community.
Explainable Artificial Intelligence (XAI)Explainable ModelsImproved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding
The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions. Unfortunately, these approaches typically bring significant increases in computationa…
Explainable ModelsMotion CompensationVideo CompressionCausality in Neural Networks -- An Extended Abstract
Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of cau…
BIG-bench Machine LearningDisentanglementExplainable ModelsFairness+1Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach
Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e.g., optimizing a process. Experimental data availability notwithstanding has inc…
Dictionary LearningExplainable ModelsNumerical IntegrationregressionAttack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning m…
Adversarial AttackAnomaly DetectionBIG-bench Machine LearningDecision Making+1Wider Vision: Enriching Convolutional Neural Networks via Alignment to External Knowledge Bases
Deep learning models suffer from opaqueness. For Convolutional Neural Networks (CNNs), current research strategies for explaining models focus on the target classes within the associated training dataset. As a result, th…
Entity AlignmentExplainable ModelsZero-Shot Learning