Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Krishnan, Narayanan C"'
In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model
Externí odkaz:
http://arxiv.org/abs/2309.13132
Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during trai
Externí odkaz:
http://arxiv.org/abs/2309.04462
Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result i
Externí odkaz:
http://arxiv.org/abs/2205.09943
In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be att
Externí odkaz:
http://arxiv.org/abs/2110.11012
Publikováno v:
Data Mining and Knowledge Discovery (S.I: Bias and Fairness) 2023
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every cluster; i.e., a balance property. Existing solutions to fair clustering ar
Externí odkaz:
http://arxiv.org/abs/2109.00708
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining the working of these deep models to improve their trustworthiness. In
Externí odkaz:
http://arxiv.org/abs/2108.13828
Autor:
Varshney, Sumit Kumar, Kumar, Jeetu, Tiwari, Aditya, Singh, Rishabh, Gunturi, Venkata M. V., Krishnan, Narayanan C.
Interpolation in Spatio-temporal data has applications in various domains such as climate, transportation, and mining. Spatio-Temporal interpolation is highly challenging due to the complex spatial and temporal relationships. However, traditional tec
Externí odkaz:
http://arxiv.org/abs/2108.06670
A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working. These methods provide an intuitive way
Externí odkaz:
http://arxiv.org/abs/2106.12773
Meta-learning (ML) has emerged as a promising direction in learning models under constrained resource settings like few-shot learning. The popular approaches for ML either learn a generalizable initial model or a generic parametric optimizer through
Externí odkaz:
http://arxiv.org/abs/2106.10642
Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers). In a real-life scenario, obtaining sample images for all possible words is also not f
Externí odkaz:
http://arxiv.org/abs/2105.15093