Project descriptions

Collaborative Filtering

A recommender system is concerned with presenting items (e.g. books on Amazon, movies at Movielens or music at lastFM) that are likely to interest the user. In collaborative filtering, we base our recommendations on the (known) preference of the user towards other items, and also take into account the preferences of other users.

Resources

All the necessary resources (including training data) are available at https://inclass.kaggle.com/c/cil-collab-filtering-2019

To participate, follow the link here

Training Data

For this problem, we have acquired ratings of 10000 users for 1000 different items. All ratings are integer values between 1 and 5 stars.

Evaluation Metrics

Your collaborative filtering algorithm is evaluated according to the following weighted criteria:


Text Sentiment Classification

The use of microblogging and text messaging as a media of communication has greatly increased over the past 10 years. Such large volumes of data amplifies the need for automatic methods to understand the opinion conveyed in a text.

Resources

All the necessary resources (including training data) are available at https://inclass.kaggle.com/c/cil-text-classification-2019

To participate, follow the link here

Training Data

For this problem, we have acquired 2.5M tweets classified as either positive or negative.

Evaluation Metrics

Your approach is evaluated according to the following criteria:


Road Segmentation

Segmenting an image consists in partitioning an image into multiple segments (formally one has to assign a class label to each pixel). A simple baseline is to partition an image into a set of patches and classify every patch according to some simple features (average intensity). Although this can produce reasonable results for simple images, natural images typically require more complex procedures that reason abut the entire image or very large windows.

Resources

All the necessary resources (including training data) are available at https://inclass.kaggle.com/c/cil-road-segmentation-2019

To participate, follow the link here

Training Data

For this problem, we provide 100 aerial images acquired from GoogleMaps. We also provide groundtruth images where each pixel is labeled as {road, background}.

Evaluation Metrics

Your approach is evaluated according to the following criteria:

Galaxy Image Generation

The goal is to train a generative model that can generate images of galaxies observed by astronomical telescopes.

Resources

All the necessary resources (including training data) are available at https://inclass.kaggle.com/c/cil-cosmology-2019

To participate, follow the link here

Training Data

For this problem, we provide 100 astronomy images. We also provide groundtruth labels where each image is labeled as {cosmology, corrupted, background}

Evaluation Metrics

Your approach is evaluated according to the following criteria:



Computational infrastructure

Use ETH's new Leonhard cluster.



Report Grading Guidlines

Your paper will be graded by two independent reviewers according to the following three criteria:

1) Quality of paper (30%)
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6.0: Good enough for submission to an international conference.
5.5: Background, method, and experiment are clear. May have minor
issues in one or two sections. Language is good. Scores and baselines are well documented.
5.0: Explanation of work is clear, and the reader is able to identify the novelty of the work. Minor issues in one or two sections. Minor problems with language. Has all the recommended sections in the howto-paper.
4.5: Able to identify contribution. Major problems in presentation of results and or ideas and or reproducibility/baselines.
4.0: Hard to identify contribution, but still there. One or two good sections should get students a pass.
3.5: Unable to see novelty. No comparison with any baselines.


2) Creativity of solution (20%)
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6.0: Elegant proposal, either making a useful assumption, studying a particular class of data, or using a novel mathematical fact.
5.5: A non-obvious combination of ideas presented in the course or published in a paper (Depending on the difficulty of that idea).
5.0: A novel idea or combination not explicitly presented in the course.
4.5: An idea mentioned in a published paper with small extensions / changes, but not so trivial to implement.
<=4.0: A trivial idea taken from a published paper.


3) Quality of implementation (20%)
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6.0: Idea is executed well. The experiments done make sense in order to answer the proposed research questions. There are no obvious experiments not done that could greatly increase clarity. The submitted code and other supplementary material is understandable, commented, complete, clean and there is a README file that explains it and describes how to reproduce your results.

Subtractions from this grade will be made if:
- the submitted code is unclear, does not run or experiments cannot be reproduced or there is no description of it
- experiments done are useless to gain understanding or of unclear nature or obviously useful experiments have been left undone
- comparison to baselines are not done