AIMC Topic: Uncertainty

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Modelling and robust controller design for an underactuated self-balancing robot with uncertain parameter estimation.

PloS one
A comprehensive literature review of self-balancing robot (SBR) provides an insight to the strengths and limitations of the available control techniques for different applications. Most of the researchers have not included the payload and its variati...

Semi-Supervised Medical Image Segmentation With Voxel Stability and Reliability Constraints.

IEEE journal of biomedical and health informatics
Semi-supervised learning is becoming an effective solution in medical image segmentation because annotations are costly and tedious to acquire. Methods based on the teacher-student model use consistency regularization and uncertainty estimation and h...

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Computers in biology and medicine
Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a signif...

Psychological AI: Designing Algorithms Informed by Human Psychology.

Perspectives on psychological science : a journal of the Association for Psychological Science
Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defin...

Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability.

Journal of chemical information and modeling
Gaussian process (GP) is a Bayesian model which provides several advantages for regression tasks in machine learning such as reliable quantitation of uncertainty and improved interpretability. Their adoption has been precluded by their excessive comp...

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.

PloS one
Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we...

Toward Intrinsic Adversarial Robustness Through Probabilistic Training.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Modern deep neural networks have made numerous breakthroughs in real-world applications, yet they remain vulnerable to some imperceptible adversarial perturbations. These tailored perturbations can severely disrupt the inference of current deep learn...

Neural-Network-Based Adaptive Control of Uncertain MIMO Singularly Perturbed Systems With Full-State Constraints.

IEEE transactions on neural networks and learning systems
This article investigates the tracking control problem for a class of nonlinear multi-input-multi-output (MIMO) uncertain singularly perturbed systems (SPSs) with full-state constraints. The underlying issues become more challenging because two-time-...

Synchronization of Uncertain Coupled Neural Networks With Time-Varying Delay of Unknown Bound via Distributed Delayed Impulsive Control.

IEEE transactions on neural networks and learning systems
This article investigates the issue of synchronization for a type of uncertain coupled neural networks (CNNs) involving time-varying delay with unmeasured or unknown bound by delayed impulsive control with distributed delay. A new Halanay-like delaye...

Adaptive prescribed settling time periodic event-triggered control for uncertain robotic manipulators with state constraints.

Neural networks : the official journal of the International Neural Network Society
In this paper, an adaptive prescribed settling time periodic event-triggered control (APST-PETC) is investigated for uncertain robotic manipulators with state constraints. In order to economize network bandwidth occupancy and reduce computational bur...