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Uncertainty

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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...

Clinical target volume delineation quality assurance for MRI-guided prostate radiotherapy using deep learning with uncertainty estimation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Previous studies on automatic delineation quality assurance (QA) have mostly focused on CT-based planning. As MRI-guided radiotherapy is increasingly utilized in prostate cancer treatment, there is a need for more research on ...

Robust Vector BOTDA Signal Processing with Probabilistic Machine Learning.

Sensors (Basel, Switzerland)
This paper presents a novel probabilistic machine learning (PML) framework to estimate the Brillouin frequency shift (BFS) from both Brillouin gain and phase spectra of a vector Brillouin optical time-domain analysis (VBOTDA). The PML framework is us...

Predictive Uncertainty Estimation for Camouflaged Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong enquote center bias of the training dataset leads to models of poor genera...

Relief in Sight? Chatbots, In-baskets, and the Overwhelmed Primary Care Clinician.

Journal of general internal medicine
The recent emergence of publically facing artificial intelligence (AI) chatbots has generated vigorous discussion in the lay public around the possibilities, liabilities, and uncertainties of the integration of such technology into everyday life. As ...