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Found 5 papers, 2 papers with code
Domain-Specific Code Language Models: Unraveling the Potential for HPC Codes and Tasks
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.
Scope is all you need: Transforming LLMs for HPC Code
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.
Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings
Learned embeddings are widely used to obtain concise data representation and enable transfer learning between different data sets and tasks.
More or Less: When and How to Build Neural Network Ensembles
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.
MotherNets: Rapid Deep Ensemble Learning
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.
