This event feed aggregates content from the Research Events feeds from the Helsinki Institute for Information Technology HIIT, Aalto University Department of Computer Science, and the University of Helsinki Department of Computer Science.
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10.02.2014 13:15–14:00
HIIT seminar
Aalto University, Computer Science Building, lecture hall T2Abstract:
A number of machine learning problems involve analysis of graphs and have borrowed extensively from graph theory - spectral clustering, graph kernels, etc. In this talk, I’ll present our recent work on establishing a connection between Lovasz theta function, a powerful concept in graph theory which has been used heavily in algorithms and... -
07.02.2014 12:00–16:00
Defence of thesis
University of Helsinki Main Building, Auditorium XIV, Unioninkatu 34
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07.02.2014 10:15–11:15
HIIT seminar
Exactum B119Title:
Information Propagation in the Bitcoin Network
Abstract:
Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how Bitcoin uses a multi-hop broadcast to propagate transactions and blocks through the... -
07.02.2014 10:15–11:15
HIIT seminar
Exactum B119Title:Information Propagation in the Bitcoin NetworkAbstract:Bitcoin is a digital currency that unlike traditional currencies does not rely on a centralized authority. Instead Bitcoin relies on a network of volunteers that collectively implement a replicated ledger and verify transactions. In this paper we analyze how... -
Collaborative Matrix Factorization for Predicting Drug-Target Interactions
Prof. Hiroshi Mamitsuka, Kyoto University, Japan
Abstract:Computationally predicting drug-target interactions is useful to discover potential new drugs (or targets). Currently, powerful machine learning approaches for this issue use not only known drug-target interactions but also drug and target similarities. Using similarities is well-accepted pharmacologically, since the two types of similarities...
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03.02.2014 14:15–15:00
HIIT seminar
Aalto University, Computer Science Building, lecture hall T3Title: Collaborative Matrix Factorization for Predicting Drug-Target
Interactions
Abstract: Computationally predicting drug-target interactions is
useful to discover potential new drugs (or targets). Currently,
powerful machine learning approaches for this issue use not only
known drug-target interactions but also drug... -
31.01.2014 10:15–11:15
HIIT seminar
Exactum B119Title of talk: Generating Ideas for Pictorial Advertisements: Starting from Pictorial Metaphors
Abstract:
Behind every good advertisement there is a creative concept, a Big Idea. In contrast to the countless number of ads, a small number of patterns of effective communication (idea templates) were uncovered, which are invariant...
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31.01.2014 10:15–11:15
HIIT seminar
Exactum B119Title of talk: Generating Ideas for Pictorial Advertisements: Starting from Pictorial Metaphors
Abstract:
Behind every good advertisement there is a creative concept, a Big Idea. In contrast to the countless number of ads, a small number of patterns of effective communication (idea templates) were uncovered, which are invariant...
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Mining Subsequences with Surprising Event Counts
Dr. Jefrey Lijffijt, Aalto University
Abstract:We consider the problem of mining subsequences with surprising event counts, which can be used, for example, to find parts of a text where a word is surprisingly frequent. We introduce a method to find all subsequences of a long data sequence of a fixed length where the count of an event is significantly different from what is expected. In estimating what is expected, we have to take into...
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27.01.2014 13:15–14:00
HIIT seminar
Aalto University, Computer Science Building, lecture hall T2Title: Mining Subsequences with Surprising Event Counts
Abstract:
We consider the problem of mining subsequences with surprising event counts, which can be used, for example, to find parts of a text where a word is surprisingly frequent. We introduce a method to find all subsequences of a long data sequence of a fixed length where the count of an event is...
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