partially supervised learning
play

Partially supervised learning Prof. Marco Loog Delft University of - PowerPoint PPT Presentation

Mar 23, 2023 •241 likes •293 views

11th VIPS Advanced School on Computer Vision, Pattern Recognition and Image Processing Partially supervised learning Prof. Marco Loog Delft University of Technology (The Netherland) University of Copenhagen (Denmark) VIPS advanced schools

  1. 11th VIPS Advanced School on Computer Vision, Pattern Recognition and Image Processing Partially supervised learning Prof. Marco Loog Delft University of Technology (The Netherland) University of Copenhagen (Denmark)

  2. VIPS advanced schools • Series of advanced schools on Pattern Recognition, Computer Vision and Image processing (from 2004), – Speakers : B. Frey, E. Trucco, M. Figueiredo, S. Soatto, H. Bischof, A. Leonardis, O. Screer, L. Van Gool, N. Jojic, A. and M. Bronstein, A. Vinciarelli, D. Gatica- Perez, B. Duin

  3. 11th School “ Partially supervised learning ” • The Speaker : Marco Loog - Delft University of Technology (NL) - University of Copenhagen (DK) Short Bio : Marco Loog received an M.Sc. degree in mathematics from Utrecht University and in 2004 a Ph.D. degree from the Image Sciences Institute for the development and improvement of contextual statistical pattern recognition methods and their use in the processing and analysis of images. He currently assistant professor in the Pattern Recognition Laboratory at Delft University of Technology, and honorary full professor in pattern recognition at the University of Copenhagen, and is also affiliated to Eindhoven University of Technology. Marco's research interests include multiscale image analysis, semi-supervised and multiple instance learning, saliency, computational perception, the dissimilarity approach, and black math.

  4. Logistic • Day 1 (19/5/2015): Morning: 11.30-13.30: AULA L (Ca Vignal 2, Floor 1) Afternoon: 14.30-16.30: AULA I (Ca Vignal 2, Floor 0) • Day 2 (20/5/2015): Morning: 11.30-13.30: AULA C (Ca Vignal 1, Floor 0) Afternoon: 14.30-16.30: AULA C (Ca Vignal 1, Floor 0) • Day 3 (21/5/2015): Morning: 9.30-13.00: Lab Alfa (Ca Vignal 2, Floor 0) Afternoon: 14.00-18.30: Lab Alfa (Ca Vignal 2, Floor 0) NOTE: Please make agreements with locals for the 21st, University will be closed!!

  5. Some information… Lab lessons : for external students: please ask us for login information (Lab Alfa: this building, ground floor, on the right), For PhD students interested in getting a PhD credit : The final project will be assigned the last day. Ask us for attendance certificate, Possibilities for lunch : - canteen (just cross the street), - bar at the Department (Bar Swan, near “ Sala Verde ”), Social Dinner on Wednesday, please book it!

Recommend

Learning frameworks Self-supervised learning: (Auto)encoder networks Supervised learning Network

Learning frameworks Self-supervised learning: (Auto)encoder networks Supervised learning Network

Learning frameworks Self-supervised learning: (Auto)encoder networks Supervised learning Network must copy inputs to outputs through Assumes environment specifies correct output (targets) for each input a bottleneck (fewer hidden units)

407 views • 5 slides
Self-Supervised Feature Learning by Learning to Spot Artifacts Wonbin Kim Self-Supervised

Self-Supervised Feature Learning by Learning to Spot Artifacts Wonbin Kim Self-Supervised

Self-Supervised Feature Learning by Learning to Spot Artifacts Wonbin Kim Self-Supervised Learning To exploit different labelings that are freely available besides or within visual data, and to use them as intrinsic reward signals to

387 views • 24 slides
Stacking for supervised learning Stacking for supervised learning Niall Rooney, NIKEL,

Stacking for supervised learning Stacking for supervised learning Niall Rooney, NIKEL,

Stacking for supervised learning Stacking for supervised learning Niall Rooney, NIKEL, University of Ulster 1 Ensemble learning Ensemble learning l Postulate multiple hypotheses to explain the data l Shortcomings of single model learning

681 views • 19 slides
Machine Learning for NLP Supervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain

Machine Learning for NLP Supervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain

Machine Learning for NLP Supervised Learning Aurlie Herbelot 2019 Centre for Mind/Brain Sciences University of Trento 1 Supervised learning Supervised: you know the result of the task you want to perform. Supervised learning mostly

832 views • 61 slides
PCA CS 446 Supervised learning So far, weve done supervised learning: Given (( x i , y i )) ,

PCA CS 446 Supervised learning So far, weve done supervised learning: Given (( x i , y i )) ,

PCA CS 446 Supervised learning So far, weve done supervised learning: Given (( x i , y i )) , find f with f ( x i ) y i . k -nn, decision trees, . . . 1 / 18 Supervised learning So far, weve done supervised learning: Given (( x i , y

1.01k views • 56 slides
Learning to Rank Learning to Rank with Partially-Labeled Data with Partially-Labeled Data Kevin

Learning to Rank Learning to Rank with Partially-Labeled Data with Partially-Labeled Data Kevin

Learning to Rank Learning to Rank with Partially-Labeled Data with Partially-Labeled Data Kevin Duh University of Washington (Joint work with Katrin Kirchhoff) 1 Motivation Motivation Machine learning can be an effective solution for

472 views • 29 slides
Semi-Supervised Learning Maria-Florina Balcan 03/30/2015 Readings: Semi-Supervised Learning.

Semi-Supervised Learning Maria-Florina Balcan 03/30/2015 Readings: Semi-Supervised Learning.

Semi-Supervised Learning Maria-Florina Balcan 03/30/2015 Readings: Semi-Supervised Learning. Encyclopedia of Machine Learning. Jerry Zhu, 2010 Combining Labeled and Unlabeled Data with Co- Training. Avrim Blum, Tom Mitchell. COLT

953 views • 45 slides
RECSM Summer School: Machine Learning for Social Sciences Session 1.3: Supervised Learning and

RECSM Summer School: Machine Learning for Social Sciences Session 1.3: Supervised Learning and

RECSM Summer School: Machine Learning for Social Sciences Session 1.3: Supervised Learning and Model Accuracy Reto West Department of Political Science and International Relations University of Geneva 1 Supervised Learning Supervised

436 views • 42 slides
CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression What is supervised learning? Supervised learning is the process of trying to infer from labeled data the underlying function that produced the labels

751 views • 25 slides
CSE 258 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 258 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 258 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression What is supervised learning? Supervised learning is the process of trying to infer from labeled data the underlying function that produced the labels

599 views • 33 slides
CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression

CSE 158 Lecture 1.5 Web Mining and Recommender Systems Supervised learning Regression What is supervised learning? Supervised learning is the process of trying to infer from labeled data the underlying function that produced the labels

784 views • 33 slides
Machine Learning Lecture 6 Unsupervised Learning with a bit of Supervised Learning Clustering

Machine Learning Lecture 6 Unsupervised Learning with a bit of Supervised Learning Clustering

Machine Learning Lecture 6 Unsupervised Learning with a bit of Supervised Learning Clustering and nearest neighbour classifiers. Justin Pearson 1 2020 1 http://user.it.uu.se/~justin/Teaching/MachineLearning/index.html 1 / 27 Supervised

813 views • 27 slides
Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce

Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce

Web Mining and Recommender Systems Supervised learning Regression Learning Goals Introduce the concept of Supervised Learning Understand the components (inputs and outputs) of supervised learning problems Introduce linear

1.36k views • 122 slides
THE SUPERVISED LEARNING PROBLEM THE SUPERVISED LEARNING PROBLEM Matthieu R Bloch January 7, 2020

THE SUPERVISED LEARNING PROBLEM THE SUPERVISED LEARNING PROBLEM Matthieu R Bloch January 7, 2020

THE SUPERVISED LEARNING PROBLEM THE SUPERVISED LEARNING PROBLEM Matthieu R Bloch January 7, 2020 1 WHY ML ? WHY ML ? Traditional engineering is top-down We use fundamental principles (mathematics, physics) to build models and abstractions

243 views • 13 slides
Weakly Supervised Classification Weakly Supervised Classification and Robust Learning and Robust

Weakly Supervised Classification Weakly Supervised Classification and Robust Learning and Robust

IIT-H and RIKEN-AIP Joint Workshop on March 15, 2019 Machine Learning and Applications, Hyderabad, India Weakly Supervised Classification Weakly Supervised Classification and Robust Learning and Robust Learning --- Overview of Our Recent

1.06k views • 25 slides
WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Tuesday,

WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Tuesday,

WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Tuesday, January 14, 2020 1 LOGISTICS LOGISTICS TAs and Office hours Monday: Mehrdad (TSRB) - 2pm-3:15pm Tuesday: TJ (VL) - 1:30pm - 2:45pm Wednesday:

378 views • 13 slides
Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site

Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site

CS11-747 Neural Networks for NLP Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site https://phontron.com/class/nn4nlp2018/ Supervised, Unsupervised, Semi-supervised Most models handled here are supervised learning

670 views • 32 slides
Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site

Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site

CS11-747 Neural Networks for NLP Unsupervised and Semi-supervised Learning of Structure Graham Neubig Site https://phontron.com/class/nn4nlp2020/ Supervised, Unsupervised, Semi-supervised Most models handled here are supervised learning

580 views • 39 slides
Supervised Learning: The Setup Machine Learning 1 Last lecture We saw What is learning?

Supervised Learning: The Setup Machine Learning 1 Last lecture We saw What is learning?

Supervised Learning: The Setup Machine Learning 1 Last lecture We saw What is learning? Learning as generalization The badges game 2 This lecture More badges Formalizing supervised learning Instance space and features

1.04k views • 80 slides
WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Thrusday

WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Thrusday

WHY SUPERVISED LEARNING MAY WORK WHY SUPERVISED LEARNING MAY WORK Matthieu R Bloch Thrusday January 9, 2020 1 LOGISTICS LOGISTICS Registration update Please decide soon if you want to take the class or not Still many people on the waiting

474 views • 23 slides
Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on supervised learning methods such as regression and classification. In that setting we observe both a set of features X 1 , X 2 , . . . , X p for each

934 views • 50 slides
Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on

Unsupervised Learning Unsupervised vs Supervised Learning: Most of this course focuses on supervised learning methods such as regression and classification. In that setting we observe both a set of features X 1 , X 2 , . . . , X p for each

721 views • 60 slides
Probabilistic Graphical Models 10-708 Learning Partially Observed Learning Partially Observed

Probabilistic Graphical Models 10-708 Learning Partially Observed Learning Partially Observed

Probabilistic Graphical Models 10-708 Learning Partially Observed Learning Partially Observed Graphical Models Graphical Models Eric Xing Eric Xing Lecture 13, Oct 26, 2005 Reading: MJ-Chap. 5,10,11 Partially observed GMs Speech

555 views • 16 slides
CSCE 478/878 Lecture 2: Supervised Learning Supervised Learning Stephen Scott Introduction

CSCE 478/878 Lecture 2: Supervised Learning Supervised Learning Stephen Scott Introduction

CSCE 478/878 Lecture 2: CSCE 478/878 Lecture 2: Supervised Learning Supervised Learning Stephen Scott Introduction Outline Learning a Stephen Scott Class from Examples Noise and Other (Adapted from Ethem Alpaydin) Problems

470 views • 21 slides

More recommend