INDUS: Effective and Efficient Language Models for Scientific Applications

no code implementations • 17 May 2024 • Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Mike Little, Elizabeth Fancher, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panagiotis Vagenas, S. Karthik Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Armin Mehrabian, Tsendgar Lee

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks.

Contrastive Learning Information Retrieval +3

A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks

no code implementations • 25 Jun 2019 • Armin Mehrabian, Mario Miscuglio, Yousra Alkabani, Volker J. Sorger, Tarek El-Ghazawi

Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition.

speech-recognition Speech Recognition

PCNNA: A Photonic Convolutional Neural Network Accelerator

no code implementations • 23 Jul 2018 • Armin Mehrabian, Yousra Al-Kabani, Volker J. Sorger, Tarek El-Ghazawi

Convolutional Neural Networks (CNN) have been the centerpiece of many applications including but not limited to computer vision, speech processing, and Natural Language Processing (NLP).