16 citations · 34 across the 6 of their papers we have counts for
6 papers
HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling
Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli
Graphs model latent variable relationships in many real-world systems, and Message Passing Neural Networks (MPNNs) are widely used to learn such structures for downstream tasks. Wh…
INDUS: Effective and Efficient Language Models for Scientific Applications
Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka +33
Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs tr…
Foundation Models for Generalist Geospatial Artificial Intelligence
Johannes Jakubik, Sujit Roy, C. E. Phillips +30
Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote se…
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
S. Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude +12
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been incr…
AB2CD: AI for Building Climate Damage Classification and Detection
Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl +1
We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset,…
TensorBank: Tensor Lakehouse for Foundation Model Training
Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel +9
Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we…