Institution
Helsinki Institute for Information Technology
Facility•Espoo, Finland•
About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.
Papers published on a yearly basis
Papers
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TL;DR: This work proposes an approach where a reinforcement learning agent is trained to make the first two decisions (i.e., rescheduling timing and computing time allocation) using neuroevolution of augmenting topologies (NEAT) as the reinforcement learning algorithm, which yields better closed-loop solutions on three out of four studied routing problems.
14 citations
TL;DR: A novel connection is pointed out: linear discriminant analysis (LDA) minimizes the overlap of the simulation chains measured by a common multivariate convergence measure, which is justified for visualizing convergence.
14 citations
TL;DR: A novel method for automatically generating slogans, given a target concept and an adjectival property to express and a metaphor interpretation model to allow generating metaphorical slogans is described.
Abstract: In advertising, slogans are used to enhance the recall of the advertised product by consumers and to distinguish it from others in the market. Creating effective slogans is a resource-consuming task for humans. In this paper, we describe a novel method for automatically generating slogans, given a target concept (e.g., car) and an adjectival property to express (e.g., elegant) as input. Additionally, a key component in our approach is a novel method for generating nominal metaphors, using a metaphor interpretation model, to allow generating metaphorical slogans. The method for generating slogans extracts skeletons from existing slogans. It then fills a skeleton in with suitable words by utilizing multiple linguistic resources (such as a repository of grammatical relations, and semantic and language models) and genetic algorithms to optimize multiple objectives such as semantic relatedness, language correctness, and usage of rhetorical devices. We evaluate the metaphor and slogan generation methods by running crowdsourced surveys. On a five-point Likert scale, we ask online judges to evaluate whether the generated metaphors, along with three other metaphors generated using different methods, highlight the intended property. The slogan generation method is evaluated by asking crowdsourced judges to rate generated slogans from five perspectives: (1) how well is the slogan related to the topic, (2) how correct is the language of the slogan, (3) how metaphoric is the slogan, (4) how catchy, attractive, and memorable is it, and (5) how good is the slogan overall. Similarly, we evaluate existing expert-made slogans. Based on the evaluations, we analyze the method and provide insights regarding existing slogans. The empirical results indicate that our metaphor generation method is capable of producing apt metaphors. Regarding the slogan generator, the results suggest that the method has successfully produced at least one effective slogan for every evaluated input.
14 citations
Proceedings Article•
07 Nov 2005TL;DR: In this paper, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies using a new probabilistic method.
Abstract: Semantic Web ontologies are based on crisp logic and do not provide well-defined means for expressing uncertainty. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies.
14 citations
TL;DR: This work introduces a Bayesian generative transfer learning model which represents similarity across document collections by sparse sharing of latent topics controlled by an Indian buffet process that outperforms the HDP approach both on synthetic data and in first of the two case studies on text collections.
14 citations
Authors
Showing all 632 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Dimitri P. Bertsekas | 94 | 332 | 85939 |
| Olli Kallioniemi | 90 | 353 | 42021 |
| Heikki Mannila | 72 | 295 | 26500 |
| Jukka Corander | 66 | 411 | 17220 |
| Jaakko Kangasjärvi | 62 | 146 | 17096 |
| Aapo Hyvärinen | 61 | 301 | 44146 |
| Samuel Kaski | 58 | 522 | 14180 |
| Nadarajah Asokan | 58 | 327 | 11947 |
| Aristides Gionis | 58 | 292 | 19300 |
| Hannu Toivonen | 56 | 192 | 19316 |
| Nicola Zamboni | 53 | 128 | 11397 |
| Jorma Rissanen | 52 | 151 | 22720 |
| Tero Aittokallio | 52 | 271 | 8689 |
| Juha Veijola | 52 | 261 | 19588 |
| Juho Hamari | 51 | 176 | 16631 |