9 citations · 13 across the 5 of their papers we have counts for
5 papers
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Aristeidis Tsaris, Isaac Lyngaas, John Lagregren +6
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images fr…
Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars
Wesley Brewer, Aditya Kashi, Sajal Dash +4
In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligen…
Pretraining Billion-scale Geospatial Foundational Models on Frontier
Aristeidis Tsaris, Philipe Ambrozio Dias, Abhishek Potnis +3
As AI workloads increase in scope, generalization capability becomes challenging for small task-specific models and their demand for large amounts of labeled training samples incre…
Ultra-Long Sequence Distributed Transformer
Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris +7
Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due t…
A study of decays to strange final states with GlueX in Hall D using components of the BaBar DIRC
The GlueX Collaboration, M. Dugger, B. Ritchie +103
We propose to enhance the kaon identification capabilities of the GlueX detector by constructing an FDIRC (Focusing Detection of Internally Reflected Cherenkov) detector utilizing…