activity
20172022
most citedDetection of Tooth caries in Bitewing Radiographs using Deep Learning

61 citations · 116 across the 15 of their papers we have counts for

collaborators

17 papers

cs.CR2022★ 4 cited

Efficient ML Models for Practical Secure Inference

Vinod Ganesan, Anwesh Bhattacharya, Pratyush Kumar +3

ML-as-a-service continues to grow, and so does the need for very strong privacy guarantees. Secure inference has emerged as a potential solution, wherein cryptographic primitives a…

cs.LG2022

Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages

Emil Biju, Anirudh Sriram, Mitesh M. Khapra +1

Gesture typing is a method of typing words on a touch-based keyboard by creating a continuous trace passing through the relevant keys. This work is aimed at developing a keyboard t…

cs.CL2021★ 1 cited

OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages

Prem Selvaraj, Gokul NC, Pratyush Kumar +1

AI technologies for Natural Languages have made tremendous progress recently. However, commensurate progress has not been made on Sign Languages, in particular, in recognizing sign…

cs.LG2021★ 2 cited

SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions

Vinod Ganesan, Gowtham Ramesh, Pratyush Kumar

Task-agnostic pre-training followed by task-specific fine-tuning is a default approach to train NLU models. Such models need to be deployed on devices across the cloud and the edge…

cs.CV2021

Machine Learning approaches to do size based reasoning on Retail Shelf objects to classify product variants

Muktabh Mayank Srivastava, Pratyush Kumar

There has been a surge in the number of Machine Learning methods to analyze products kept on retail shelves images. Deep learning based computer vision methods can be used to detec…

cs.CL2021

On the Prunability of Attention Heads in Multilingual BERT

Aakriti Budhraja, Madhura Pande, Pratyush Kumar +1

Large multilingual models, such as mBERT, have shown promise in crosslingual transfer. In this work, we employ pruning to quantify the robustness and interpret layer-wise importanc…