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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

792
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
792
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K
Neural Circuits01:25

Neural Circuits

1.8K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.8K
Neural Regulation01:37

Neural Regulation

40.5K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
40.5K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1000
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1000
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning.

The Journal of chemical physics·2024
Same author

Machine Learning Methods for Multiscale Physics and Urban Engineering Problems.

Entropy (Basel, Switzerland)·2022
See all related articles

Related Experiment Video

Updated: Oct 12, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

78

Winsorization for Robust Bayesian Neural Networks.

Somya Sharma1, Snigdhansu Chatterjee2

  • 1Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street SE, Minneapolis, MN 55455, USA.

Entropy (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Winsorization enhances model performance against data outliers. Deep learning models show more robustness to outliers than Gaussian processes, even without Winsorization.

Keywords:
Bayesian neural networkWinsorizationconcrete dropoutflipoutmixture density networksuncertainty quantificationvariational Gaussian process

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Related Experiment Videos

Last Updated: Oct 12, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

78
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.1K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.5K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • The rise of big data and deep learning necessitates understanding model robustness to noise and outliers.
  • Aberrant observations can significantly degrade the performance of machine learning models.

Purpose of the Study:

  • To evaluate the effectiveness of Winsorization in improving model performance on outlier-prone data.
  • To compare the robustness of various probabilistic artificial intelligence and machine learning techniques against outliers.

Main Methods:

  • Comparative analysis of probabilistic machine learning techniques.
  • Application of Winsorization as a data preprocessing step.
  • Supervised learning case studies to assess model performance.

Main Results:

  • Winsorization is a versatile technique for mitigating the impact of outliers.
  • Gaussian processes demonstrate high vulnerability to outliers.
  • Deep learning techniques generally exhibit greater robustness to outliers compared to Gaussian processes, with or without Winsorization.

Conclusions:

  • Winsorization can recover model performance in the presence of outliers.
  • The choice of machine learning technique significantly influences robustness to data anomalies.
  • Deep learning methods offer a more robust approach for handling noisy datasets.