Computer Vision Group Prof. Daniel Cremers

Current Trends in Machine Learning

Jan Stühmer, Christina Lichtenthäler,

Jürgen Sturm, Daniel Cremers

Preparation Meeting

What You Shall Learn In The Seminar

Get an overview on current trends in machine learning

Read and understand scientific publications

Write a scientific report

Prepare and give a talk

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Important Dates

Choose your topic until 17.10.2012 (First come first Serve!)

Deadline for the report: 01.03.2012

Dates for the talks: 10.01.2013

17.01.2013

24.01.2013

31.01.2013

First Meeting: 18.10.2012 (mandatory!) Fixed assignment of topic and date

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Preparation

Please do not work on your topic completely alone

Meet at least twice with your supervisor

Recommended schedule

1 month before your talk: Meet your supervisor and discuss paper

1 week before your talk: Meet your supervisor to discuss your slides

[optional] after the talk: Feedback of your supervisor regarding the talk

1 week before 01.03.13: Submit a draft of your report

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Report and Talk

Send PDF (not PPTX, not DOC) via email to your supervisor, Latex template available on the web-page

Recommended length: 6-8 pages

Required: Minimum 6, Maximum 10 pages

Language: English or German

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Hints for Your Talk

20 min. + 5–10 min. for discussion

Don’t put too much information on one slide

1-2 min. per slide 10-20 slides

Recommended structure

Introduction, Problem Motivation, Outline

Approach

Experimental results

Discussion

Summary of (scientific) contributions

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Evaluation Criterions

Gained expertise in the topic

Quality of your talk

Quality of the report

Active participation in the seminar is required (ask questions, comment talks)

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Regular Attendance Is Required

Attendance at each appointment is necessary

In case of absence:

Medical attest

Or: One-sided abstract of the presented papers

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Overview of availabe Topics

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Human motion recognition using support vector machines

[Cao, Masoud,Boley,Papanikolopoulos; CVIU ‘07]

Recognition of human motions in videoclips

Motion is represented as a set of filtered images

ML Method: Support vector Machines

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Online Action Recognition with Wrapped Boosting

[Nejigane, Shimosaka, Mori, Sato; IROS‘07]

Recognition of motions like running and walking in motion capture data gathered by a magnetic motion capturing system

ML Methods: Wrapped Boosting

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BPR: Bayesian Personalized Ranking from Implicit Feedback

[Rendle, Freudenthaler,Gantner, Schmidt-Thieme; UAI ‘09]

Calculating personalized product recommendation from implicit feedback

Learn a personalized ranking of products

ML Methods: Matrix Factorization

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Semantic Place Classification of Indoor Environments with Mobile Robots using Boosting

[Rottmann,Mozos, Stachniss, Burgard; AI ‘05]

Classification of places, which enables for a robot to regognize the place where it is located

ML Methods: Boosting and Hidden Markov Model HMM

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Probabilistic Pointing Target Prediction via Inverse Optimal Control

[Ziebart, Dey, Bagnell; IUI 2012]

Learn to predict the target of the motion of a computer mouse

Features: Velocity profile, user preferences, …

Inverse reinforcement learning

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Real-Time Human Pose Recognition in Parts from Single Depth Images

[Shotton, Fitzgibbon, Cook, Sharp, Finocchio, Moore, Kipman, Blake; CVPR 2011]

Recognize pose from depth images (Kinect)

Learn good features for depth images

Learn decision trees to recognize body parts

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Building High-level Features Using Large Scale Unsupervised Learning

[Le, Ranzato, Monga, Devin, Chen, Corrado, Dean, Ng; ICML 2012]

Learn high-level feature detectors

Autoencoder network

10 million unlabeled images from Google

Discovery: Specific neurons for faces, cats, …

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Machine Learning That Matters [Wagstaff, ICML 2012]

Claim: Machine learning research has lost its connection to real problems

Inspiring discussion on the future of ML research

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Blind Source Separation Techniques for the Decomposition of Multiply Labeled Fluorescence Images

[Neher et al.; Biophysical Journal 2009]

In microscopy, images of different fluorescent molecules that have a distinct characteristic spectrum of the emmited light need to be processed

Because the spectra of the molecules are overlapping, the signals of the different molecules lead to a superimposed image of the different sources

Blind source seperation, a machine learning techinque based on Non-negative Matrix Factorization, can be used to seperate the signals of the different molecules

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Variational Message Passing [Winn, Bishop; Journal of Machine Learning Research 2005]

Graphical models are a powerful tool to model knowledge in a probabilistic fashion

This paper describes a variational message passing algorithm for inference in Bayesian networks based on factorization of the graph, that can handle more general problems than classic belief propagation

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Boosting classifiers for drifting concepts [Scholz, Klinkenberg; Intelligent Data Analysis 2007]

Time-Varying data recently attracted a lot of interest in the machine learning community

This work introduces a boosting-like method, that can train an ensemble of classifiers from a data stream

This „concept drift“ allows to handle data, which underlying distribution is changing over time

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Gaussian Processes in Machine Learning [Rassmussen; Advanced Lectures in Machine Learning 2004]

Gaussian Process models can be used to formulate a Bayesian framework for regression

This paper is a short tutorial and explaines the stochastic process, how gaussian processes are used in supervised learning and the role of hyperparameters in the covariance function

It is a good starting point for an interested student for further research

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Enjoy the seminar!

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