GBX9MO23 - Information Access and Retrieval

2020-2021 subject: gbx9mo23-2020-2021-exam-tmpkxtz.pdf

Available from Master 2 MOSIG and MSIAM. Course Description

This course is given by Jean-Pierre Chevallet, Philippe Mulhem, Lorraine Goeuriot and Georges Quénot from the Multimedia Information Modeling and Retrieval (MRIM) research group of the Grenoble Informatics Laboratory (LIG).

Contact: Georges.Quenot@imag.fr

Contents / schedule:

Part I. Foundations of Information Retrieval (Jean-Pierre Chevallet and Philippe Mulhem)

• Course 1: Information retrieval basics (J.-P. Chevallet).
• Course 2: Classical models for information retrieval (J.-P. Chevallet).
• Course 3: Vector space model and embeddings (J.-P. Chevallet)
• Course 4: Probabilistic IR Models (version for 2020) (P. Mulhem). 2 exercices with corrections

Part II: Web, social networks and health (Philippe Mulhem, Lorraine Goeuriot)

• Course 5: Web information retrieval (version for 2020) (P. Mulhem). NOTE : a mail has been sent to all the students regarding the Zoom link for the remote lesson. If you did not receive it, please send a mail to Philippe Mulhem. Excel document with the computations of Hits and Pagerank values of the slides. Excel document with the example of logistic regression for Learning to Rank of the slides.
• Course 6: IR evaluation (2020 version)(L. Goeuriot / P. Mulhem). Excel document with examples of Recall/precision tables and diagrams, and nDCG.
• Course 7: Medical information retrieval (L. Goeuriot).
• Course 8: Personalization and social information retrieval (2020 version) (P. Mulhem).

Part III: Multimedia indexing and retrieval (Georges Quénot)

• Mathematics Reminders: Linear algebra and convolutions (G. Quénot).
• Mathematics Reminders: Differential calculus (G. Quénot).
• Course 9: Visual content representation and retrieval (only introduction and color / texture / points of interest descriptors) (G. Quénot).
• Course 10: Classical machine Learning for multimedia indexing (part on LSH is excluded) (G. Quénot).
• Course 11-12: Deep learning for multimedia indexing and retrieval (G. Quénot).

Video recodings

You can find the videos of courses 5, 6, 8, 9 and 10 on INP Chamillo, under ENSIMAG WMM9MO23 Information access and retrieval/Documents.
You can get the videos of courses 11 and 12 from the MRIM server at:
http://mrim.imag.fr/georges.quenot/gbx9mo23/Course_11_Deep_learning_for_multimedia_indexing_and_retrieval_part_1.mp4
http://mrim.imag.fr/georges.quenot/gbx9mo23/Course_12_Deep_learning_for_multimedia_indexing_and_retrieval_part_2.mp4
The other courses were not recorded.

Reference to IR books or papers

• Introduction to Information Retrieval, http://nlp.stanford.edu/IR-book/
• Search Engines Information Retrieval in Practice, https://ciir.cs.umass.edu/irbook/

The examination will be on February 1st, 2021 from 9 to 11am.

Course materials, the two papers related to the examinations, personal notes, and calculators (without network capabilities) are allowed.

You will have to answer questions on topics that occur in the lessons. You are expected to do a research work on the two papers proposed below, in a way to understand them and to be able to comment then. You must also take time to read complementary information in order to understand the papers. Be sure to bring with you a copy of the two research papers as they will NOT be redistributed with the examination subject. These can be annotated by you. The bibliography and appendices, if any, are part of the papers.

• F. Faghri, D. J. Fleet, J. R. Kiros, S. Fidler,
  VSE++: Improving Visual-Semantic Embeddings with Hard Negatives,
  Proceedings of the British Machine Vision Conference (BMVC), 2018. (BMVC Spotlight)
  https://arxiv.org/pdf/1707.05612
• Zamani, H., Dehghani, M., Croft, W. B., Learned-Miller, E., & Kamps, J. (2018, October).
  From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing.
  Proceedings of the 27th ACM international conference on information and knowledge management (pp. 497-506).
  https://people.cs.umass.edu/~elm/papers/zamani.pdf

2017-2018 examination: gbx9mo23-2017-2018-exam.pdf, papers:
https://www.researchgate.net/publication/305081616_A_Simple_Enhancement_for_Ad-hoc_Information_Retrieval_via_Topic_Modelling,
http://www.tyr.unlu.edu.ar/tallerIR/2014/papers/novel-tfidf.pdf,
http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf

2018-2019 examination: gbx9mo23-2018-2019-exam.pdf, papers:
https://danluu.com/bitfunnel-sigir.pdf,
https://arxiv.org/pdf/1604.01325.

2019-2020 examination: gbx9mo23-2019-2020-exam.pdf, papers:
http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Dual_Encoding_for_Zero-Example_Video_Retrieval_CVPR_2019_paper.pdf
https://ciir-publications.cs.umass.edu/pub/web/getpdf.php?id=1302

The second session examination will take place on April 16, from 8am to 10am. It will be an oral examination and it will he held remotely via zoom with these links:

• Jean-Pierre Chevallet: https://univ-grenoble-alpes-fr.zoom.us/my/chevallj
• Georges Quénot: https://univ-grenoble-alpes-fr.zoom.us/my/quenotg

You will individually receive an email with the exact time at which you should reach these rooms. You will have 15 minutes with Georges Quénot and 15 minutes with Jean-Pierre Chevallet. There is no new paper to study for this session. Questions and/or exercises will be related to the papers selected for session 1 (above) with some related to the course in relation with the papers.