no code implementations • NAACL (sdp) 2021 • Yash Gupta, Pawan Sasanka Ammanamanchi, Shikha Bordia, Arjun Manoharan, Deepak Mittal, Ramakanth Pasunuru, Manish Shrivastava, Maneesh Singh, Mohit Bansal, Preethi Jyothi
Large pretrained models have seen enormous success in extractive summarization tasks.
no code implementations • 29 Sep 2023 • Harika Abburi, Tanya Chaudhary, Haider Ilyas, Lakshmi Manne, Deepak Mittal, Don Williams, Derek Snaidauf, Edward Bowen, Balaji Veeramani
Rolling bearing fault diagnosis has garnered increased attention in recent years owing to its presence in rotating machinery across various industries, and an ever increasing demand for efficient operations.
1 code implementation • 27 Jun 2022 • Debottam Dutta, Debarpan Bhattacharya, Sriram Ganapathy, Amir H. Poorjam, Deepak Mittal, Maneesh Singh
In this paper, we describe an approach for representation learning of audio signals for the task of COVID-19 detection.
no code implementations • 11 Jun 2022 • Deepak Mittal, Amir H. Poorjam, Debottam Dutta, Debarpan Bhattacharya, Zemin Yu, Sriram Ganapathy, Maneesh Singh
This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge.
no code implementations • 30 Sep 2021 • Arjit Jain, Pranay Reddy Samala, Deepak Mittal, Preethi Jyoti, Maneesh Singh
Time masking has become a de facto augmentation technique for speech and audio tasks, including automatic speech recognition (ASR) and audio classification, most notably as a part of SpecAugment.
1 code implementation • IJCAI 2021 • Arjit Jain, Pranay Reddy Samala, Preethi Jyothi, Deepak Mittal, Maneesh Singh
The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image.
Image Augmentation Semi Supervised Learning for Image Captioning
no code implementations • 23 May 2020 • VSR Veeravasarapu, Abhishek Goel, Deepak Mittal, Maneesh Singh
Contour shape alignment is a fundamental but challenging problem in computer vision, especially when the observations are partial, noisy, and largely misaligned.
no code implementations • 25 Sep 2019 • VSR Veeravasarapu, Deepak Mittal, Abhishek Goel, Maneesh Singh
In this work, we devise a curriculum-learning-based training process for object boundary detection.
1 code implementation • 26 Dec 2018 • Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra, Balaraman Ravindran
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.
1 code implementation • 31 Jan 2018 • Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra, Balaraman Ravindran
In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned.