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Found 9 papers, 4 papers with code
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc.
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information commonly encountered in real-world scenarios, remains in its infancy.
Transforming the Hybrid Cloud for Emerging AI Workloads
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability.
Prithvi WxC: Foundation Model for Weather and Climate
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting.
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
TensorBank: Tensor Lakehouse for Foundation Model Training
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
Recent advances in object detection have benefited significantly from rapid developments in deep neural networks.
Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
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.
