AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

no code implementations • 19 Sep 2023 • S. Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude, Johannes Jakubik, Anne Jones, Nam Nguyen, Christopher Phillips, Sujit Roy, Shraddha Singh, Campbell Watson, Raghu Ganti, Hendrik Hamann, Udaysankar Nair, Rahul Ramachandran, Kommy Weldemariam

In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.

Language Modelling Operator learning +4

TensorBank: Tensor Lakehouse for Foundation Model Training

no code implementations • 5 Sep 2023 • Romeo Kienzler, Leonardo Pondian Tizzei, Benedikt Blumenstiel, Zoltan Arnold Nagy, S. Karthik Mukkavilli, Johannes Schmude, Marcus Freitag, Michael Behrendt, Daniel Salles Civitarese, Naomi Simumba, Daiki Kimura, Hendrik Hamann

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language.

Lifelong Object Detection

no code implementations • 2 Sep 2020 • Wang Zhou, Shiyu Chang, Norma Sosa, Hendrik Hamann, David Cox

Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.

Knowledge Distillation Object +3

Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images

no code implementations • 30 Sep 2017 • Sergiy Zhuk, Tigran Tchrakian, Albert Akhriev, Siyuan Lu, Hendrik Hamann

The prediction phase consists of utilizing a linear transport equation, which describes the propagation of COD images in the fluid flow predicted by NSE, to estimate the future motion of the COD images.

Motion Forecasting Optical Flow Estimation