This research details a new architecture for designing and deploying intelligent metasurfaces that leverage machine learning operations and deep generative models to optimize wireless signal control.
A new training algorithm leverages inherent sparsity in subgradients to dramatically improve the efficiency and robustness of Max-Plus networks.
New research explores how offline reinforcement learning can optimize wireless communication networks, even in unpredictable environments.
A new approach transforms the complex task of ad ranking into a streamlined process, boosting performance and reducing delays.
Modern AI excels at pattern recognition, but a fundamental gap in understanding how we think-what researchers are calling ‘cognitive dark matter’-explains its persistent limitations.
New research explores how generative AI can improve decision-making in management, while highlighting the critical need to address inherent biases and ensure human validation.
A new approach to explaining graph neural network decisions focuses on intelligently altering the input graph to reveal key influencing factors.
New research using agent-based modeling reveals the surprising consequences of widespread adoption of different investment approaches.
A new machine learning framework uses autoencoders to sift through vast collections of stellar spectra, identifying both data errors and potentially rare astronomical phenomena.
A new analysis reveals that static assessments of anti-money laundering controls in cryptocurrency markets are misleading, as performance erodes due to evolving illicit finance tactics.