Explore our peer-reviewed research on cutting-edge AI advancements and breakthroughs
Journal of AI Research, 2024 • 37 citations
This paper proposes a novel neuroevolution framework combining evolutionary algorithms with modern neural architecture search techniques to achieve state-of-the-art results on 10+ benchmark datasets.
IEEE Transactions on Quantum AI, 2024
This article presents a breakthrough in integrating quantum computing frameworks with classical deep learning models, achieving 85% accuracy on complex pattern recognition tasks.
Title | Authors | Publication | Action |
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Hybrid Quantum-Classical Approaches to Image Recognition
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Dr. Jane Smith et al. | Nature AI | Download PDF |
Neuroplastic Evolution Algorithms
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Dr. John Lee, Marcus Zhang | ACM Transactions on AI | Download PDF |
Dynamic Network Optimization Framework
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Dr. Emily Chen et al. | IEEE TNNLS | Download PDF |
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