Pulkit Sharma
Indian Institute of Technology Mandi
48 Papers
151 Citations
Pulkit Sharma is an academic researcher from Indian Institute of Technology Mandi. The author has contributed to research in topics: Sparse approximation & Computer science. The author has an hindex of 10, co-authored 41 publications. Previous affiliations of Pulkit Sharma include University of Oxford & University of Massachusetts Amherst.
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Papers
Deep Interpretable Early Warning System for the Detection of Clinical Deterioration
TL;DR: The ‘Deep Early Warning System’ (DEWS) is proposed, an interpretable end-to-end deep learning model that interpolates temporal data and predicts the probability of an adverse event, defined as the composite outcome of cardiac arrest, mortality or unplanned ICU admission.
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Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality
TL;DR: The results show that training the model in the federated learning framework leads to comparable performance to the traditional centralised setting in the state-of-the-art performance while maintaining data privacy.
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Learning Hierarchy Aware Embedding From Raw Audio for Acoustic Scene Classification
Vinayak Abrol,Pulkit Sharma +1 more
TL;DR: This work proposes a raw waveform based end-to-end ASC system using convolutional neural network that leverages the hierarchical relations between acoustic categories to improve the classification performance and uses a prototypical model.
Greedy dictionary learning for kernel sparse representation based classifier
TL;DR: Compared to the existing state-of-the-art methods, the proposed method has much less computational complexity, but performs similar for various pattern classification tasks.
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Voiced/nonvoiced detection in compressively sensed speech signals
TL;DR: The proposed novel unsupervised voiced/nonvoiced (V/NV) detection method attempts to exploit the fact that there is significant glottal activity during production of voiced speech while the same is not true for nonvoiced speech, and provides compelling evidence of the effectiveness of sparse feature vector for V/NV detection.
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