Results 131 to 140 of about 21,965 (264)

Loss Behavior in Supervised Learning With Entangled States

open access: yesAdvanced Quantum Technologies, EarlyView.
Entanglement in training samples supports quantum supervised learning algorithm in obtaining solutions of low generalization error. Using analytical as well as numerical methods, this work shows that the positive effect of entanglement on model after training has negative consequences for the trainability of the model itself, while showing the ...
Alexander Mandl   +4 more
wiley   +1 more source

Output Feedback Design for Parameter Varying Systems Subject to Persistent Disturbances and Control Rate Constraints

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper develops a framework for designing output feedback controllers for constrained linear parameter‐varying systems that experience persistent disturbances. We specifically propose an incremental parameter‐varying output feedback control law to address control rate constraints, as well as state and control amplitude constraints.
Jackson G. Ernesto   +2 more
wiley   +1 more source

The Pier Luigi Nervi's concrete structure of Palazzetto dello Sport: Modeling and dynamic characterization

open access: yesStructural Concrete, EarlyView.
Abstract This paper presents a numerical and experimental study aimed at the modeling and dynamic characterization of the reinforced concrete structure of the Palazzetto dello Sport in Rome, designed and by Pier Luigi Nervi with Annibale Vitellozzi, and built by Nervi & Bartoli contractors in 1956‐57.
Jacopo Ciambella   +2 more
wiley   +1 more source

Pairing particles into holonomies. [PDF]

open access: yesSci Adv
Neef V   +3 more
europepmc   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang   +5 more
wiley   +1 more source

Boosted unsupervised feature selection for tumor gene expression profiles

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi   +5 more
wiley   +1 more source

Home - About - Disclaimer - Privacy