no code implementations • 12 Feb 2024 • Sabyasachi Ghosh, Ajit Rajwade
This impedes recovery of signals which may have sparse representations in the GFT bases of the ground truth graph.
no code implementations • 12 May 2023 • Sabyasachi Ghosh, Sanyam Saxena, Ajit Rajwade
The computational cost of running the QMPNN and the CS algorithms is significantly lower than the cost of using a neural network with the same number of parameters separately on each image to classify the images, which we demonstrate via extensive experiments.
1 code implementation • 16 May 2020 • Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika, Ritesh Goenka, Ajit Rajwade, Manoj Gopalkrishnan
Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data.
2 code implementations • ICLR 2020 • Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, Sunita Sarawagi
Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.
1 code implementation • IJCNLP 2019 • Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, Vihari Piratla
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).
Ranked #15 on Grammatical Error Correction on CoNLL-2014 Shared Task
Grammatical Error Correction Optical Character Recognition (OCR)