High Diagram to Diagram Variability

High diagram-to-diagram variability, encompassing inconsistencies in data representations across different instances or runs, poses a significant challenge across diverse scientific domains. Current research focuses on mitigating this variability through methods like robust statistical testing, machine learning techniques (including neural networks and meta-learning), and the development of standardized diagramming languages for improved reproducibility and analysis. Addressing this variability is crucial for enhancing the reliability and trustworthiness of scientific findings, particularly in fields like deep learning, quantum computing, and medical image analysis, where inconsistencies can hinder model development, validation, and deployment.

Papers

September 7, 2024

Chemical Power Variability among Microscopic Robots in Blood Vessels
Tad Hogg
Fuel Cell Micro Robot High Diagram to Diagram Variability Microvascular Network

August 9, 2024

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver, Oscar Hernandez, Mark Coletti, Ada Sedova
Research Reproducibility Deep Learning Application High Performance Computing Impact Assessment High Diagram to Diagram Variability Spatial Accelerator Non Associativity

June 7, 2024

Robust quantum dots charge autotuning using neural network uncertainty
Victor Yon, Bastien Galaup, Claude Rohrbacher, Joffrey Rivard, Clément Godfrin, Ruoyu Li, Stefan Kubicek, Kristiaan De Greve, Louis Gaudreau, Eva Dupont-Ferrier, Yann Beilliard, Roger G. Melko, Dominique Drouin
Neural Network Quantum Dot Charge Tuning High Diagram to Diagram Variability Spin Qubit

February 8, 2024

Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures
Vincent Abbott
General Analysis Deep Learning Architecture Timely Communication Neural Circuit Fundamental Diagram Deep Learning Community High Diagram to Diagram Variability

December 4, 2023

Reconsideration on evaluation of machine learning models in continuous monitoring using wearables
Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B Nahab, Xiao Hu
Machine Learning Machine Learning Model Global Evaluation Wearable Device Health Monitoring High Diagram to Diagram Variability

November 23, 2023

Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects
Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan
Machine Learning Model Model Selection Bias Metric BIAS Detection Machine Learning Project High Diagram to Diagram Variability

October 1, 2023

Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks
Sinjini Banerjee, Reilly Cannon, Tim Marrinan, Tony Chiang, Anand D. Sarwate
Neural Network Deep Neural Network Stochastic Optimization Hypothesis Testing High Diagram to Diagram Variability

October 11, 2022

Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection
Cholmin Kang, Chunggi Lee, Heon Song, Minuk Ma, S ergio Pereira
Histopathological Image Inter Rater Cell Annotation High Diagram to Diagram Variability

April 27, 2022

Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection
Wenbin Song, Di Wu, Weiming Shen, Benoit Boulet
Anomaly Detection Meta Learning Fault Detection Shot Anomaly Detection Rolling Bearing High Diagram to Diagram Variability

High Diagram to Diagram Variability

Papers

Chemical Power Variability among Microscopic Robots in Blood Vessels

Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications

Robust quantum dots charge autotuning using neural network uncertainty

Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures

Reconsideration on evaluation of machine learning models in continuous monitoring using wearables

Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects

Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks

Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection

Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection