Social media algorithms driven by AI shape far more than what appears on our screens, including how we think, feel, and ...
Data-driven AI systems increasingly influence our choices, raising concerns about autonomy, fairness, and accountability. Achieving algorithmic autonomy requires new infrastructures, motivation ...
Abstract: Over the past decades, extensive research has been conducted on adversarial attacks and defense mechanisms in deep learning, particularly in real-world applications such as autonomous ...
High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...
ABSTRACT: Visual Sensor Networks (VSNs) focus on capturing data, extracting relevant information, and enabling communication. However, the presence of obstacles affects network efficiency, linking ...
ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
A new evolutionary technique from Japan-based AI lab Sakana AI enables developers to augment the capabilities of AI models without costly training and fine-tuning processes. The technique, called ...
Mikel Hernaez receives funding from the Spanish Ministry of Science, Innovation and Universities, the government of Navarra, the EU Department of Defence, the Carlos III Health Institute and the ...
Abstract: Dynamic constrained multiobjective optimization problems (DCMOPs) are widely existed in real-world applications and emerged as a prominent research focus in the evolutionary computation ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results