INSTITUTIONAL DIGITAL REPOSITORY

Browsing Research Publications by Author "Krishnan, N.C."

Browsing Research Publications by Author "Krishnan, N.C."

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  • Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet) 
    Garg, J.; Peri, S.V.; Tolani, H.; Krishnan, N.C. (2018-11-28)
    Learning from different modalities is a challenging task. In this paper, we look at the challenging problem of cross modal face verification and recognition between caricature and visual image modalities. Caricature ...
  • Explainable supervised domain adaptation 
    Kamakshi, V.; Krishnan, N.C. (2022-12-09)
    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. ...
  • Machine learning methods trained on simple models can predict critical transitions in complex natural systems 
    Deb, S.; Sidheekh, S.; Clements, C.F.; Krishnan, N.C.; Dutta, P.S. (2022-07-15)
    Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical ...
  • MAIRE - A model-agnostic interpretable rule extraction procedure for explaining classifiers 
    Sharma, R.; Reddy, N.; Kamakshi, V.; Krishnan, N.C.; Jain, S. (2022-09-03)
    The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier’s output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance ...
  • Multi-label learning for activity recognition 
    Kumar, R.; Qamar, I.; Virdi, J.S.; Krishnan, N.C. (2016-11-18)
    Advances in pervasive and ubiquitous computing have resulted in the development of sensors that can be easily deployed in the natural habitat of a human to acquire activity related data. However, inferring meaningful ...
  • Multi-task deep learning for predicting poverty from satellite images 
    Pandey, S.M.; Agarwal, T.; Krishnan, N.C. (2019-05-20)
    Estimating economic and developmental parameters such as poverty levels of a region from satellite imagery is a challenging problem that has many applications. We propose a two step approach to predict poverty in a rural ...
  • Multi-task deep learning for predicting poverty from satellite images 
    Pandey, S.M.; Agarwal, T.; Krishnan, N.C. (2022-09-30)
    Estimating economic and developmental parameters such as poverty levels of a region from satellite imagery is a challenging problem that has many applications. We propose a two step approach to predict poverty in a rural ...
  • One-class classification-based real-time activity error detection in smart homes 
    Das, B.; Cook, D.J.; Krishnan, N.C.; Schmitter-Edgecombe, M. (2016-11-17)
    Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide ...
  • RACOG and wRACOG: Two probabilistic oversampling techniques 
    Das, B.; Krishnan, N.C.; Cook, D.J. (2016-11-19)
    As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in ...
  • Supervised heterogeneous transfer learning using random forests 
    Sukhija, S.; Krishnan, N.C.; Kumar, D. (2018-12-20)
    Supervised transfer learning algorithms utilize labeled data from auxiliary domains for learning in another domain where labeled data is scarce or absent. Given sufficient cross-domain corresponding instances, one can ...
  • Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks 
    Singh Sambyal, A.; Krishnan, N.C.; Bathula, D.R. (2022-06-23)
    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 ...

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