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Found 4 papers, 0 papers with code
Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem
The No-Free-Lunch (NFL) theorem, which quantifies problem- and data-independent generalization errors regardless of the optimization process, provides a foundational framework for comprehending diverse learning protocols' potential.
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods.
Information Cocoons in Online Navigation
Social media and online navigation bring us enjoyable experience in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information.
Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects.
