Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.

ORB: An efficient alternative to SIFT or SURF

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces. For data sets of size n living in R d , the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such as the approximate minimum spanning tree. The article is based on the material from the authors' STOC'98 and FOCS'01 papers. It unifies, generalizes and simplifies the results from those papers.

/pdf/approximate-nearest-neighbors-towards-removing-the-curse-of-27lmlz5prr.pdf

Approximate nearest neighbors: towards removing the curse of dimensionality

Given a labeled graph, the frequent-subgraph mining (FSM) problem asks to find all the k-vertex subgraphs that appear with frequency greater than a given threshold FSM has numerous applications ranging from biology to network science, as it provides a compact summary of the characteristics of the graph However, the task is challenging, even more so for evolving graphs due to the streaming nature of the input and the exponential time complexity of the problem In this paper, we initiate the study of the approximate FSM problem in both incremental and fully-dynamic streaming settings, where arbitrary edges can be added or removed from the graph For each streaming setting, we propose algorithms that can extract a high-quality approximation of the frequent k-vertex subgraphs for a given threshold, at any given time instance, with high probability In contrast to the existing state-of-the-art solutions that require iterating over the entire set of subgraphs for any update, our algorithms operate by maintaining a uniform sample of k-vertex subgraphs with optimized neighborhood-exploration procedures local to the updates We provide theoretical analysis of the proposed algorithms and empirically demonstrate that the proposed algorithms generate high-quality results compared to baselines

/pdf/mining-frequent-patterns-in-evolving-graphs-2jhclnjf8v.pdf

Mining Frequent Patterns in Evolving Graphs

Large networks are being generated by applications that keep track of relationships between different data entities Examples include online social networks recording interactions between individuals, sensor networks logging information exchanges between sensors, and more There is a large body of literature on computing exact or approximate properties on large networks, although most methods assume static networks On the other hand, in most modern real-world applications, networks are highly dynamic and continuous interactions along existing connections are generated Furthermore, it is desirable to consider that old edges become less important, and their contribution to the current view of the network diminishes over time

We study the problem of maintaining the neighborhood profile of each node in an interaction network Maintaining such a profile has applications in modeling network evolution and monitoring the importance of the nodes of the network over time We present an online streaming algorithm to maintain neighborhood profiles in the sliding-window model The algorithm is highly scalable as it permits parallel processing and the computation is node centric, hence it scales easily to very large networks on a distributed system, like Apache Giraph We present results from both serial and parallel implementations of the algorithm for different social networks The summary of the graph is maintained such that query of any window length can be performed

/pdf/maintaining-sliding-window-neighborhood-profiles-in-2rdik0zg1w.pdf

Maintaining sliding-window neighborhood profiles in interaction networks

We propose a new scalable algorithm for the facility-location problem. We study the graph setting, where the cost of serving a client from a facility is represented by the shortest-path distance on a graph. This setting is applicable to various problems arising in the Web and social media, and allows to leverage the inherent sparsity of such graphs. To obtain truly scalable performance, we design a parallel algorithm that operates on clusters of shared-nothing machines. In particular, we target modern Pregel-like architectures, and we implement our algorithm on Apache Giraph. Our work builds upon previous results: a facility location algorithm for the PRAM model, a recent distance-sketching method for massive graphs, and a parallel algorithm to finding maximal independent sets. The main challenge is to adapt those building blocks to the distributed graph setting, while maintaining the approximation guarantee and limiting the amount of distributed communication. Extensive experimental results show that our algorithm scales gracefully to graphs with billions of edges, while, in terms of quality, being competitive with state-of-the-art sequential algorithms.

/pdf/scalable-facility-location-for-massive-graphs-on-pregel-like-455sev2uii.pdf

Scalable Facility Location for Massive Graphs on Pregel-like Systems

Allowing users to find pages on the web similar to a particular query page is a crucial component of modern search engines. A variety of techniques and approaches exist to support "Related Pages" queries. In this paper we discuss shortcomings of previous approaches and present a unifying approach that puts special emphasis on the use of text, both within anchors and surrounding anchors. In the central contribution of our paper, we present a novel technique for automating the evaluation process, allowing us to tune our parameters to maximize the quality of the results. Finally, we show how to scale our approach to millions of web pages, using the established Locality-Sensitive-Hashing technique.

/pdf/similarity-search-on-the-web-evaluation-and-scalability-47468yf4y7.pdf

Similarity Search on the Web: Evaluation and Scalability Considerations

Polarized topics often spark discussion and debate on social media. Recent studies have shown that polarized debates have a specific clustered structure in the endorsement net- work, which indicates that users direct their endorsements mostly to ideas they already agree with. Understanding these polarized discussions and exposing social media users to con- tent that broadens their views is of paramount importance. The contribution of this demonstration is two-fold. (i) A tool to visualize retweet networks about controversial issues on Twitter. By using our visualization, users can understand how polarized discussions are shaped on Twitter, and explore the positions of the various actors. (ii) A solution to reduce polarization of such discussions. We do so by exposing users to information which presents a contrarian point of view. Users can visually inspect our recommendations and understand why and how these would play out in terms of the retweet network. Our demo (https://users.ics.aalto.fi/kiran/reducingControversy/ homepage) provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers. The ideas in the demo can also help content providers design tools to broaden their reach to people with different political and ideological backgrounds.

/pdf/mary-mary-quite-contrary-exposing-twitter-users-to-2os4vflw11.pdf

Aristides Gionis

Papers

Mining Frequent Patterns in Evolving Graphs

Maintaining sliding-window neighborhood profiles in interaction networks

Scalable Facility Location for Massive Graphs on Pregel-like Systems

Similarity Search on the Web: Evaluation and Scalability Considerations

Mary, Mary, Quite Contrary: Exposing Twitter Users to Contrarian News