background data resampling for outlier aware
play

Background Data Resampling for Outlier-Aware Classification - PowerPoint PPT Presentation

Feb 02, 2023 •19 likes •159 views

Background Data Resampling for Outlier-Aware Classification Out-of-distribution Detection OOD Detection with Background Data OOD Detection with Background Data OOD Detection

  1. Background Data Resampling for Outlier-Aware Classification

  2. Out-of-distribution Detection ● ○ ○ ● ○ ○ ○

  3. OOD Detection with Background Data ● ○ ○ ●

  4. OOD Detection with Background Data ● ○ ○

  5. OOD Detection with Background Data ●

  6. Background Data Resampling ● ○ ○ ○

  7. Background Data Resampling ● ○ ○ ○

  8. Background Data Resampling ● ○ ○ ●

  9. Experiments ● ○ ○ ○

  10. Experiments ●

  11. Experiments ● ○

  12. Experiments ● ○ ○

  13. Experiments ● ○ ○

  14. Conclusions ● ○ ○ ● ○ ○ ● ○ ○ ○

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Outlier-aware Dictionary Learning for Sparse Representation Sajjad Amini Mostafa Sadeghi

Outlier-aware Dictionary Learning for Sparse Representation Sajjad Amini Mostafa Sadeghi

Outlier-aware Dictionary Learning for Sparse Representation Sajjad Amini Mostafa Sadeghi Mohsen Joneidi Massoud Babaie-Zadeh Christian Jutten Department of Electrical Engineering, Sharif University of Technology, Tehran,

672 views • 17 slides
Outlier Detection Outlier detection is both easy and difficult. It is easy since there are

Outlier Detection Outlier detection is both easy and difficult. It is easy since there are

Outlier Detection Outlier detection is both easy and difficult. It is easy since there are several relatively straightforward tests for the presence of outliers. It is difficult since there are no firm rules as to when outlier removal is

210 views • 20 slides
Proximity-based Outlier Detection Objects far away from the others are outliers The

Proximity-based Outlier Detection Objects far away from the others are outliers The

Proximity-based Outlier Detection Objects far away from the others are outliers The proximity of an outlier deviates significantly from that of most of the others in the data set Distance-based outlier detection: An object o is an

416 views • 39 slides
Outlier Outlier Outlier- Outlier - -robust - robust robust robust identification

Outlier Outlier Outlier- Outlier - -robust - robust robust robust identification

GESG seminar, 16 October 2015, UFM Outlier Outlier Outlier- Outlier - -robust - robust robust robust identification identification identification of identification of of of switching regimes: switching regimes: switching regimes:

582 views • 27 slides
DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles Yue Zhao Maciej K.

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles Yue Zhao Maciej K.

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles Yue Zhao Maciej K. Hryniewicki Department of Computer Science Data Assurance & Analytics University of Toronto PricewaterhouseCoopers Outlier Ensembles Intro Proposal

213 views • 17 slides
Estimating the Performance of Predictive Models with Resampling Methods Florian Pargent

Estimating the Performance of Predictive Models with Resampling Methods Florian Pargent

Estimating the Performance of Predictive Models with Resampling Methods Florian Pargent (Florian.Pargent@psy.lmu.de) Ludwig-Maximilians-Universitt Mnchen 1 Why Do We Need Resampling? How Does Resampling Work? How To Avoid Common Mistakes?

598 views • 34 slides
Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml

Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml

Introduction to Machine Learning Tuning: Nested Resampling compstat-lmu.github.io/lecture_i2ml NESTED RESAMPLING Just like we can generalize holdout splitting to resampling to get more reliable estimates of the predictive performance, we can

545 views • 7 slides
Outlier Detection Chapter 12 of Data Mining: Concepts and Techniques JIAWEI HAN, MICHELINE KAMBER,

Outlier Detection Chapter 12 of Data Mining: Concepts and Techniques JIAWEI HAN, MICHELINE KAMBER,

3/15/2017 Outlier Detection Chapter 12 of Data Mining: Concepts and Techniques JIAWEI HAN, MICHELINE KAMBER, JIAN PEI PRESENTED BY: SHERRY ZHU EECS6412 WINTER 2017 MARCH 15, 2017 OUTLIER DETECTION CHAPTER 12 OF DATA MINING: CONCEPTS AND TECHNIQUES

933 views • 25 slides
Real-Time Resampling Processor for SWARM Mark Peryer Harvard Smithsonian Center for Astrophysics

Real-Time Resampling Processor for SWARM Mark Peryer Harvard Smithsonian Center for Astrophysics

Real-Time Resampling Processor for SWARM Mark Peryer Harvard Smithsonian Center for Astrophysics August 16, 2017 Presentation Overview Background Objectives Design Results Future work Background Event Horizon Telescope (EHT) Image the

318 views • 27 slides
Parametric bootstrap August 30, 2017 Resampling from the data or from distribution Simple

Parametric bootstrap August 30, 2017 Resampling from the data or from distribution Simple

Resampling from the data or from distribution Simple Example Spline Example Parametric bootstrap August 30, 2017 Resampling from the data or from distribution Simple Example Spline Example Bootstrap + Monte Carlo = Parametric Bootstrap

226 views • 12 slides
Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013

Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013

Variance Estimation for Survey-Weighted Data Using Bootstrap Resampling Methods: 2013 Methods-of-Payment Survey Questionnaire Heng Chen and Q. Rallye Shen 2017 STATA The views expressed in this presentation are those of the author. No

249 views • 10 slides
Outlier Detection for Temporal DATA: A Survey Manish Gupta et al. presented by Seoul

Outlier Detection for Temporal DATA: A Survey Manish Gupta et al. presented by Seoul

Outlier Detection for Temporal DATA: A Survey Manish Gupta et al. presented by Seoul National University ga0408@snu.ac.kr July 06, 2018 1/50 Table of Contents 1. Introduction 2.

632 views • 50 slides
Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier Detection Automatically identify data points that are somehow different from the rest Working assumption: There are considerably

905 views • 74 slides
Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier Detection Automatically identify data points that are somehow different from the rest Working assumption: There are considerably

984 views • 72 slides
Outlier Detection Motivation: Fraud Detection http://i.imgur.com/ckkoAOp.gif Jian Pei: CMPT

Outlier Detection Motivation: Fraud Detection http://i.imgur.com/ckkoAOp.gif Jian Pei: CMPT

Outlier Detection Motivation: Fraud Detection http://i.imgur.com/ckkoAOp.gif Jian Pei: CMPT 741/459 Data Mining -- Outlier Detection (1) 2 Techniques: Fraud Detection Features Dissimilarity Groups and noise

878 views • 30 slides
Combining information from different sources: A resampling based approach S.N. Lahiri Department

Combining information from different sources: A resampling based approach S.N. Lahiri Department

Combining information from different sources: A resampling based approach S.N. Lahiri Department of Statistics North Carolina State University May 17, 2013 Overview Background Examples/Potential applications Theoretical Framework Combining

338 views • 33 slides
Challenge of Reproducible Pipelines Pjotr Prins 11th Biohackathon 2018 Matsue, Japan, December

Challenge of Reproducible Pipelines Pjotr Prins 11th Biohackathon 2018 Matsue, Japan, December

Challenge of Reproducible Pipelines Pjotr Prins 11th Biohackathon 2018 Matsue, Japan, December 9th, UMC Utrecht/UTHSC GeneNetwork.org p. 1 Challenge Reproducible analysis starts with software p. 2 Deployment Software deployment is

237 views • 11 slides
Reproducible Research The Hacker Within Monday 15 th October 2018 Simon Branford Advertising

Reproducible Research The Hacker Within Monday 15 th October 2018 Simon Branford Advertising

Reproducible Research The Hacker Within Monday 15 th October 2018 Simon Branford Advertising vs. Scholarship Jon Claerbout: An article about computational results is advertising, not scholarship. The actual scholarship is the full

399 views • 21 slides
Reproducible Builds Valerie Young (spectranaut) Linux Conf Australia 2016 Reproducible Builds

Reproducible Builds Valerie Young (spectranaut) Linux Conf Australia 2016 Reproducible Builds

Reproducible Builds Valerie Young (spectranaut) Linux Conf Australia 2016 Reproducible Builds What if you could always compile free software? Valerie Young (spectranaut) Linux Conf Australia 2016 Valerie Young F96E 6B8E FF5D 372F FDD1 DA43

743 views • 60 slides
Tutorial on Floating-Point Analysis and Reproducibility Tools for Scientific Software Ignacio

Tutorial on Floating-Point Analysis and Reproducibility Tools for Scientific Software Ignacio

Tutorial on Floating-Point Analysis and Reproducibility Tools for Scientific Software Ignacio Laguna, Harshitha Menon Lawrence Livermore National Laboratory Michael Bentley, Ian Briggs, Pavel Panchekha, Ganesh Gopalakrishnan University of Utah

311 views • 7 slides
Ekaterina Nosova DMI Dept of Mathematics and Informatics, University of Salerno, Italy

Ekaterina Nosova DMI Dept of Mathematics and Informatics, University of Salerno, Italy

Ekaterina Nosova DMI Dept of Mathematics and Informatics, University of Salerno, Italy Outline Introduction to biclustering problem. Data Sets Biclustering Task of biclustering Bicluster definition. Combinatoric

516 views • 25 slides
Stochastic simulation and resampling methods Statistical modelling: Theory and practice Gilles

Stochastic simulation and resampling methods Statistical modelling: Theory and practice Gilles

Stochastic simulation and resampling methods Statistical modelling: Theory and practice Gilles Guillot gigu@dtu.dk October 22, 2013 Gilles Guillot (gigu@dtu.dk) Stochastic simulation and resampling methods October 22, 2013 1 / 32

475 views • 30 slides
Synthe'c data in US Census CompSci 590.03 Instructor: Ashwin

Synthe'c data in US Census CompSci 590.03 Instructor: Ashwin

Synthe'c data in US Census CompSci 590.03 Instructor: Ashwin Machanavajjhala Lecture 18: 590.03 Fall 13 1 Synthe'c Data Rather than have a system

375 views • 37 slides
Update of noise filtering in protoDUNE Wenqiang Gu (BNL) Carlos Sarasty (University of

Update of noise filtering in protoDUNE Wenqiang Gu (BNL) Carlos Sarasty (University of

Update of noise filtering in protoDUNE Wenqiang Gu (BNL) Carlos Sarasty (University of Cincinnati) ProtoDUNE Sim/Reco Meeting 1 Overview of the noise filtering Noise filtering (NF) is a key step towards a high-quality signal processing

321 views • 14 slides

More recommend