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

132
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...
132
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

793
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...
793
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

606
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...
606
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

79
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
79
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K

You might also read

Related Articles

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

Sort by
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.

Frontiers in neuroscience·2026
Same author

Toward real-time alignment of 3D CT and 2D X-ray with multi-stage CNNs.

Computer assisted surgery (Abingdon, England)·2026
Same author

EchoAgent: guideline-centric reasoning agent for echocardiography measurement and interpretation.

International journal of computer assisted radiology and surgery·2026
Same author

Recent innovations in placental MRI: Integrating visualization and functional imaging.

Placenta·2026
Same author

Point tracking as a temporal Cue for robust myocardial segmentation in echocardiography videos.

International journal of computer assisted radiology and surgery·2026
Same journal

Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.

Advances in neural information processing systems·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
Same journal

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same journal

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.

Advances in neural information processing systems·2026
See all related articles

Related Experiment Video

Updated: Aug 20, 2025

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K

PEP: Parameter Ensembling by Perturbation.

Alireza Mehrtash1,2, Purang Abolmaesumi1, Polina Golland3

  • 1ECE Department, University of British Columbia (UBC), Vancouver, BC.

Advances in Neural Information Processing Systems
|November 23, 2022
PubMed
Summary
This summary is machine-generated.

Parameter Ensembling by Perturbation (PEP) enhances deep network performance and calibration by creating ensembles from perturbed parameters. This method optimizes variance for improved log-likelihood and prediction accuracy without special training.

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

665
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.8K

Related Experiment Videos

Last Updated: Aug 20, 2025

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies

Published on: May 9, 2019

5.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

665
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.8K

Area of Science:

  • Deep Learning
  • Machine Learning
  • Computer Vision

Background:

  • Ensembling deep networks improves predictive performance and calibration.
  • Existing methods often require complex training procedures or architectural changes.

Purpose of the Study:

  • Introduce Parameter Ensembling by Perturbation (PEP) for deep network optimization.
  • Enhance predictive performance and calibration of deep neural networks.
  • Investigate the relationship between PEP and model overfitting.

Main Methods:

  • Constructing ensembles via Gaussian perturbations of optimal parameters.
  • Optimizing a single variance parameter to maximize validation log-likelihood.
  • Analyzing the "PEP effect" in relation to likelihood curvature and Fisher information.

Main Results:

  • PEP consistently improves model calibration and log-likelihood.
  • Achieved substantial calibration gains and mild accuracy improvements on ImageNet models (ResNet, DenseNet, Inception).
  • Demonstrated PEP's utility in probing overfitting on MNIST and CIFAR-10 benchmarks.

Conclusions:

  • PEP offers a simple yet effective method for enhancing deep network performance and calibration.
  • The technique requires no specialized training or architecture, even for pre-trained models.
  • PEP provides insights into model overfitting and can be applied universally to deep learning models.