Researchers are leveraging the power of large language models to automatically generate complex neural network architectures, creating a valuable resource for improving AI reliability and adaptability.
![Across oceanic basins from 2012 to 2017, a comparative analysis using Convolutional Neural Networks (CNN) and U-Nets demonstrates a strong correlation between remotely sensed chlorophyll saturation [latex]log(ChlSat)[/latex] and reconstructed chlorophyll concentrations [latex]log(Chl)[/latex], indicating the potential for accurate chlorophyll estimation via these distinct deep learning architectures.](https://arxiv.org/html/2602.04689v1/Fig2.png)
New research shows deep neural networks can accurately predict phytoplankton biomass-a key indicator of ocean health-by learning from physical ocean conditions.
Researchers have developed a convolutional operator network capable of modeling and predicting the complex dynamics of plasma turbulence, opening new avenues for understanding and controlling this pervasive phenomenon.
As Large Language Models become increasingly integrated into complex systems, a robust framework for identifying and mitigating potential hazards is critical.
Researchers have developed a novel approach to predict credit exposures and systemic vulnerabilities within the rapidly evolving landscape of decentralized finance.
![The aggregate measure of systemic risk, represented by the ASRI, is decomposed into its constituent parts-stablecoin risk, DeFi liquidity risk, contagion risk, and arbitrage opacity-each contributing proportionally to the overall stress, and shifts in these relative contributions during crisis periods illuminate the dominant channels of systemic transmission, adhering to the decomposition property where [latex] \sum_{i} w_{i} S_{i} [/latex] equals the total ASRI at any given time.](https://arxiv.org/html/2602.03874v1/x1.png)
Researchers have developed a comprehensive index to measure systemic risk within the rapidly evolving cryptocurrency landscape.
Researchers are leveraging the power of artificial intelligence to better understand economic choices by analyzing the underlying reasoning revealed in survey responses.
New research shows that deep learning models can effectively identify galaxy mergers, even when subtle tidal features are the key indicators.
A new approach combines code history, knowledge graphs, and AI agents to pinpoint the root causes of bugs with greater accuracy.
New research dives into the ‘black box’ of deep learning models to understand why they predict lung cancer risk, going beyond simply measuring if they are correct.