323,600 research outputs found
Prediction of relevance of an image from a scan pattern
No full textThis report considers the task of inferring implicit relevance feedback from eye movements in image retrieval settings. The feasibility of solving the problem without using any image-level features is demonstrated on two different search settings, and the accuracy of inferring the relevance feedback is shown to be relatively high, clearly better than random. In addition, the report provides a list of image-level features that are good cues for relevance
Engineering a delegatable and error-Tolerant algorithm for counting small subgraphs
No full textWe study the problem of counting the number of occurrences of a given six-vertex pattern graph S in an n-vertex host graph H. We engineer an open-source GPU implementation of a distributed algorithm design of Björklund and Kaski [PODC 2016] where (i) the execution of the algorithm can be delegated [Goldwasser, Kalai, and Rothblum, J. ACM 2015] to produce a noninteractive probabilistically checkable proof of correctness, and (ii) the execution of the algorithm when preparing the proof tolerates a controllable number of adversarial errors. Experiments with NVIDIA Tesla K80 and Tesla P100 Accelerators demonstrate that the framework is practical for inputs of up to 512 vertices, with proof checking being several orders of magnitude more efficient than preparing the proof; however, proof preparation still carries at least one order of magnitude overhead compared with just solving the problem.Peer reviewe
Alien Registration- Kaski, Hilma S. (Temple, Franklin County)
No full texthttps://digitalmaine.com/alien_docs/19358/thumbnail.jp
Active Learning in Self-Organizing Maps
No full textHasenjäger M, Ritter H, Obermayer K. Active Learning in Self-Organizing Maps. In: Oja E, Kaski S, eds. Kohonen Maps. Amsterdam: Elsevier; 1999: 57-70
Learning to Rank Images from Eye Movements
No full textCombining multiple information sources can improve the accuracy of search in information retrieval. This paper presents a new image search strategy which combines image features together with implicit feedback from users' eye movements, using them to rank images. In order to better deal with larger data sets, we present a perceptron formulation of the Ranking Support Vector Machine algorithm. We present initial results on inferring the rank of images presented in a page based on simple image features and implicit feedback of users. The results show that the perceptron algorithm improves the results, and that fusing eye movements and image histograms gives better rankings to images than either of these features alone
Accelerating Kernel Neural Gas
No full textSchleif F-M, Gisbrecht A, Hammer B. Accelerating Kernel Neural Gas. In: Kaski S, Honkela T, Gitolami M, Dutch W, eds. ICANN'2011. 2011
Neural Networks and Machine Learning in Bioinformatics - Theory and Applications
No full textSeiffert U, Hammer B, Kaski S, Villmann T. Neural Networks and Machine Learning in Bioinformatics - Theory and Applications. In: Verleysen M, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications; 2006: 521-532
The Grouped Author-Topic Model for Unsupervised Entity Resolution
No full textThis paper describes a generative approach for tackling the problem of identity resolution in a completely unsupervised context with no fixed assumption regarding the true number of identities. The problem of entity resolution involves associating different references to authors (in a paper's author list, for example) with real underlying identities. The references may be written in differing forms or may have errors, and identical references may refer to different real identities. The approach taken here uses a generative model of both the abstract of a document and its list of authors to resolve identities in a corpus of documents. In the model, authors and topics are associated with latent groups. For each document, an abstract and an author list are generated conditioned on a given group. Results are presented on real-world datasets, and outperform the best performing unsupervised methods.</p
Predicting relevance of parts of an image
No full textThis report studies the task of inferring which parts of an image are relevant for the user viewing the image. The relevance is inferred from gaze trajectory of users viewing the images given a specific task. Novel computational models based on both Bayesian generative modeling and kernel methods are developed for inferring the regions of interest from raw fixation data, as well as from combination of eye movements and image content features
Detailed-level modelling of influence spreading on complex networks
No full textPublisher Copyright: © The Author(s) 2024.The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.Peer reviewe
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