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Found 17 papers, 5 papers with code
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022.
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup.
Safety Guaranteed Control for Spacecraft Inspection Mission
In order to address this issue, we propose a control strategy based on control barrier functions, summarized as "safety check on kinematics" and "velocity tracking on dynamics" approach.
Adaptive Compatible Performance Control for Spacecraft Attitude Control under Motion Constraints with Guaranteed Accuracy
To tackle this problem, we propose a modified framework called Compatible Performance Control (CPC), which integrates the Prescribed Performance Control (PPC) scheme with a contradiction detection and alleviation strategy.
Composite Triggered Intermittent Control for Constrained Spacecraft Attitude Tracking
Further, the basic intermittent attitude controller is extended to a "constrained version" by introducing a strictly bounded virtual control law and an input saturation compensation auxiliary system.
Weighted Point Cloud Normal Estimation
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures.
When Do Curricula Work in Federated Learning?
Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL.
Event-Triggered Intermittent Prescribed Performance Control for Spacecraft Attitude Reorientation
The prescribed performance control (PPC) scheme is often employed for the control with guaranteed performance.
Neural Routing in Meta Learning
Inspired by the widely recognized finding in neuroscience that distinct parts of the brain are highly specialized for different types of tasks, we aim to improve the model performance of the current meta learning algorithms by selectively using only parts of the model conditioned on the input tasks.
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data Subspaces
This small set of principal vectors is provided to the server so that the server can directly identify distribution similarities among the clients to form clusters.
Robust Control For Spacecraft Attitude Tracking Under Multiple Physical Limitations with Guaranteed Performance
Based on the proposed structure, a backstepping controller is developed accordingly using a double-layer PPC framework.
Spacecraft Attitude Pointing Control under Pointing Forbidden Constraints with Guaranteed Accuracy
To resolve this problem, a switching controller structure is proposed in this paper based on the reduced-attitude representation, fusing the artificial potential field (APF) methodology and the Prescribed Performance Control (PPC) scheme together.
FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distribution
Classical federated learning approaches yield significant performance degradation in the presence of Non-IID data distributions of participants.
Deep Point Cloud Normal Estimation via Triplet Learning
It consists of two phases: (a) feature encoding which learns representations of local patches, and (b) normal estimation that takes the learned representation as input and regresses the normal vector.
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks
Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first-layer rules to form the decision rule set.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
Reduced expression of minimal infinite reduced words of affine Weyl groups
For an infinite Coxeter system, one can extend the weak right order to the set of infinite reduced words.
Group Theory Representation Theory
