Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

374
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
374
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Pragmatic Communication in Multi-Agent Collaborative Perception.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Posterior Sampling with Latent Diffusion for Microwave Brain Imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

Roadmap on Label-Free Super-Resolution Imaging.

Laser & photonics reviews·2024
Same author

Detecting bone lesions in X-ray under diverse acquisition conditions.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Interpretable Neural Networks for Video Separation: Deep Unfolding RPCA With Foreground Masking.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023

Related Experiment Videos

Deep Unfolding of Tail-Based Methods for Robust Sparse Recovery Under Noise and Model Mismatch.

Yhonatan Kvich, Pagoti Reshma, Pradyumna Pradhan

    IEEE Transactions on Neural Networks and Learning Systems
    |December 9, 2025
    PubMed
    Summary

    This study introduces a deep unfolding framework for sparse recovery algorithms, enhancing performance and robustness, especially under noisy conditions. The new methods offer computational efficiency and adaptability for compressed sensing tasks.

    Related Experiment Videos

    Area of Science:

    • Signal Processing
    • Machine Learning
    • Compressed Sensing

    Background:

    • Classical sparse recovery algorithms like ISTA and FISTA face limitations in performance and robustness, particularly under noisy conditions.
    • Existing deep unfolding techniques improve upon classical methods but can be further enhanced.
    • Tail-based methods offer iterative support estimation, a key advantage for refining signal recovery.

    Purpose of the Study:

    • To introduce a novel deep unfolding framework for Tail-iterative soft thresholding algorithm (ISTA) and Tail-fast ISTA (FISTA).
    • To extend classical sparse recovery algorithms into learned architectures, improving upon existing unfolding techniques.
    • To enhance recovery performance and noise robustness by integrating iterative support estimation into a deep unfolding framework.

    Main Methods:

    • Developed a deep unfolding framework integrating tail-based iterative support estimation.
    • Compared the proposed methods against classical solvers (FISTA, Tail-FISTA) and deep unfolding techniques (LISTA, DU-FISTA).
    • Evaluated performance across various sparsity levels, dynamic ranges, and noiseless/noisy conditions, including perturbed sensing matrices.

    Main Results:

    • Achieved slightly lower performance than classical solvers in noiseless cases but with significantly reduced computational costs.
    • Demonstrated resilience and improved recovery rates under heavy noise and high numbers of nonzero elements where classical methods struggle.
    • Outperformed classical sparse recovery algorithms in noisy scenarios with perturbed sensing matrices, showcasing generalization capabilities.

    Conclusions:

    • The proposed deep unfolding framework offers computational efficiency, robustness to noise, and adaptability for linear sparse recovery tasks in compressed sensing.
    • Integrating iterative support estimation into deep unfolding techniques provides significant advantages over traditional and existing deep unfolding methods.
    • The framework is general and applicable to various compressed sensing applications, highlighting the potential of learned architectures combined with iterative refinement.