6 papers
Absent, Not Faint: Fisher-Information Limits and a Logarithmic Measurement-Design Cure for Passive Characterization of Coherent Qubit Noise
Yi Pan, Meng Hsiu Tsai, Weihang You +6
Calibrating a quantum processor means estimating error parameters, and estimation theory usually assumes a parameter hard to estimate is faint: its signal is weak but present, so m…
World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
Arif Hassan Zidan, Yi Pan, Hanqi Jiang +23
World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, ena…
Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
Afrar Jahin, Yi Pan, Yingfeng Wang +2
Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical…
Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach
Afrar Jahin, Arif Hassan Zidan, Wei Zhang +2
With the rapid advancement of Artificial Intelligence (AI), Large Language Models (LLMs) have significantly impacted a wide array of domains, including healthcare, engineering, sci…
Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study
Wei Zhang, Arif Hassan Zidan, Afrar Jahin +2
While gradient-based optimizers that incorporate randomization often showcase superior performance on complex optimization, the theoretical foundations underlying this superiority…
HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
Wei Zhang, Arif Hassan Zidan, Afrar Jahin +2
Momentum-based gradients are essential for optimizing advanced machine learning models, as they not only accelerate convergence but also advance optimizers to escape stationary poi…