2 code implementations • 20 Dec 2023 • Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren
Specifically, we start off with HPC as a domain and build an HPC-specific LM, named MonoCoder, that is orders of magnitude smaller than existing LMs but delivers similar, if not better performance, on non-HPC and HPC tasks.
2 code implementations • 18 Aug 2023 • Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren
With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks.
no code implementations • 15 Dec 2022 • Tarikul Islam Papon, Abdul Wasay
Learned embeddings are widely used to obtain concise data representation and enable transfer learning between different data sets and tasks.
no code implementations • ICLR 2021 • Abdul Wasay, Stratos Idreos
We identify a critical part of this design space that is not well-understood: That is how to decide between the alternatives of expanding a single network model or increasing the number of networks and using them together in an ensemble.
no code implementations • 12 Sep 2018 • Abdul Wasay, Brian Hentschel, Yuze Liao, Sanyuan Chen, Stratos Idreos
We propose MotherNets to enable higher accuracy and practical training cost for large and diverse neural network ensembles: A MotherNet captures the structural similarity across some or all members of a deep neural network ensemble which allows us to share data movement and computation costs across these networks.