no code implementations • Findings (EMNLP) 2021 • Tianda Li, Ahmad Rashid, Aref Jafari, Pranav Sharma, Ali Ghodsi, Mehdi Rezagholizadeh
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one.
no code implementations • 28 Feb 2024 • Bhargav Ghanekar, Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan
Our resulting CADS imaging system demonstrates improvement of >1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
no code implementations • 30 Jun 2023 • Pranav Sharma, Jigyasa Singh Katrolia, Jason Rambach, Bruno Mirbach, Didier Stricker, Juergen Seiler
Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras.
no code implementations • COLING 2022 • Mehdi Rezagholizadeh, Aref Jafari, Puneeth Salad, Pranav Sharma, Ali Saheb Pasand, Ali Ghodsi
A case in point is that the best performing checkpoint of the teacher might not necessarily be the best teacher for training the student in KD.
no code implementations • 4 Oct 2021 • Pranav Sharma, Venkataramana Ajjarapu, Umesh Vaidya
The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable.
1 code implementation • 13 Sep 2021 • Tianda Li, Ahmad Rashid, Aref Jafari, Pranav Sharma, Ali Ghodsi, Mehdi Rezagholizadeh
Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge of a large neural network into a smaller one.
1 code implementation • EACL 2021 • Aref Jafari, Mehdi Rezagholizadeh, Pranav Sharma, Ali Ghodsi
Knowledge distillation (KD) is a prominent model compression technique for deep neural networks in which the knowledge of a trained large teacher model is transferred to a smaller student model.
no code implementations • 25 Jun 2020 • Pranav Sharma
While this approach is one of the fastest to market, the monolithic design makes it very hard to scale beyond a point.
no code implementations • 24 Oct 2019 • Subhrajit Sinha, Pranav Sharma, Venkataramana Ajjarapu, Umesh Vaidya
In this paper, we provide a purely data-driven technique for small-signal stability classification (voltage or angle stability) and influence characterization for a power network.