Abhijeet Sudhakar develops efficient Mamba model training for machine learning, improving sequence modelling and ...
A Chinese AI company's more frugal approach to training large language models could point toward a less energy-intensive—and more climate-friendly—future for AI, according to some energy analysts. "It ...
A new technical paper titled “Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention” was published by DeepSeek, Peking University and University of Washington.
On Friday, OpenAI made o3-mini, the company's most cost-efficient AI reasoning model so far, available in ChatGPT and the API. OpenAI previewed the new reasoning model last December, but now all ...
In my previous article, I discussed the role of data management innovation in improving data center efficiency. I concluded with words of caution and optimism regarding the growing use of larger, ...
In the intricate dance of balancing efficiency and performance within AI projects, the selection among sparse, small and large models isn't just a technical decision—it's a strategic imperative that ...
Data is fundamental to hydrological modeling and water resource management; however, it remains a major challenge in many ...
Uncertainty quantification (UQ) is a field of study that focuses on understanding, modeling, and reducing uncertainties in computational models and real-world systems. It is widely used in engineering ...
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