Special Sessions

SISAP 2020 will feature the following three special sessions:

Special session papers will supplement the regular research papers and be included in the proceedings of SISAP 2020, which will be published by Springer as a volume in the Lecture Notes in Computer Science (LNCS) series.

Special session submissions may include vision/position papers, which will be evaluated based on the quality of the arguments and ideas proposed in the papers. In order to ensure high quality of the conference papers, all papers submitted to special sessions will be peer-reviewed, including papers solicited by the special session chairs. If a special session has many high-quality submissions, some of the submissions may potentially be moved to regular sessions; likewise, relevant accepted submissions may be moved into special sessions.

Artificial Intelligence and Similarity

Special Session organized by Giuseppe Amato (ISTI-CNR, Italy), Fabrizio Falchi (ISTI-CNR, Italy), Claudio Gennaro (ISTI-CNR, Italy), and Fabio Carrara (ISTI-CNR, Italy)

Recent years have witnessed a strong renovated interest in artificial intelligence. This has been mainly due to the outstanding performance offered by deep learning methods, thanks to the innovative architecture, the availability of huge quantities of training data and the high computing power provided by GPU architectures.

There are noteworthy relationships between methods of artificial intelligence and similarity search. For example, similarity search is more often executed on features (for instance image features) extracted using artificial intelligence methods, rather than hand-crafted methods. This poses new challenges, given that such features have generally much higher (intrinsic) dimensionality, than hand-crafted features. Artificial intelligence has also been used as an instrument for building efficient and effective methods for similarity search. Consider for instance methods of metric learning, learning to index, and learning to hash. In this case, artificial intelligence is used as an alternative to hand-crafted structures and data coding to obtain efficient and effective similarity search algorithms. In addition, consider that what is generally behind many machine learning methods is the possibility of comparing, judging relationships, and estimating similarity among objects or entities in order to classify, recognize, and take decisions.

In this special session, we seek contributions where artificial intelligence and similarity evaluation/searching is either exploited in synergy or supporting one the other. Both mature research papers and position papers are welcome.

Topics include, but are not limited to:

Papers submitted to this special session must follow the regular paper submission and author guidelines of SISAP 2020 (please check out the submission guidelines). Papers will be submitted in PDF format through EasyChair; please be sure to select “Special Session: Artificial Intelligence and Similarity” in the appropriate field of the submission form.

Adversarial Machine Learning & Similarity (AMLS)

Special Session organized by Laurent Amsaleg (CNRS, France), Michael E. Houle (NII, Japan)

Most machine learning techniques are very sensitive to adversarial perturbations, in that their outputs can be corrupted through the addition of a small amount of carefully crafted noise. Why such tiny content modifications succeed in producing severe errors is still not well understood. Defensive mechanisms have been proposed to increase the robustness of the training and test phases, reducing the sensitivity of machine learning approaches to attacks based on adversarial samples. Initial investigation into this issue has concentrated on applications involving images, computer vision, and classification.

In this SISAP special session, we solicit research that tackles the issues of adversarial perturbation in machine learning, from the perspective of similarity search and its applications. The goal of AMLS being to expose the SISAP community to such issues and to foster discussions, we also welcome vision and position papers as well as papers presenting contributions in their early stages.

Topics include, but are not limited to:

Papers submitted to this special session must follow the regular paper submission and author guidelines of SISAP 2020 (please check out the submission guidelines). Papers will be submitted in PDF format through EasyChair; please be sure to select “Special session: Adversarial Machine Learning & Similarity” in the appropriate field of the submission form.

Similarity Techniques in Machine Learning (SiTe-ML)

Special Session organized by Rasmus Pagh (IT University of Copenhagen, Denmark), Anshumali Shrivastava (Rice University, USA), and Sanjiv Kumar (Google Research, USA)

Similarity Search (SS) is a fundamental operation in data processing applications. Research on SS has resulted in a large number of theoretical and algorithmic developments in getting around and understanding the fundamental hardness of SS. This has led to the development of novel fundamental ideas such as tree-based space partitioning, metric space similarity search, and randomized locality-sensitive hashing (LSH). Modern machine learning algorithms are generally dealing with high-dimensional data representations. They need to be robust to noise to be robust and to generalize well to unseen data. It is perhaps no surprise that methods developed for SS can be deployed to make ML algorithms more scalable. Such transfer of knowledge and techniques has been gaining momentum in recent years.

Ideas of getting around the curse of dimensionality from similarity search, in particular, LSH and randomized trees, have broken computational barriers in classical statistical estimations such as partition function estimation, online gradient estimation, anomaly detection, and deep learning.

The natural need for fast SS, especially maximum inner product search, has led a flurry of work in using SS for recommendation engines and deep learning with very large output space popularly known as extreme classification. Extreme classification is one of the prime facilitators of modern success in natural language processing.

The special session aims to explore the above mentioned ideas and related applications, also providing a feedback loop to researchers on similarity techniques that may inspire new investigations. Research, application as well as position papers are welcome.

Topics include, but are not limited to:

Papers submitted to this special session must follow the regular paper submission and author guidelines of SISAP 2020 (please check out the submission guidelines). Papers should be submitted in PDF format through EasyChair; please be sure to select “Special session: Similarity Techniques in Machine Learning” in the appropriate field of the submission form.