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» MUSETTE: a framework for knowledge capture from experience
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ATAL
2008
Springer
14 years 11 months ago
Learning to interact: connecting perception with action in virtual environments
Modeling synthetic characters which interact with objects in dynamic virtual worlds is important when we want the agents to act in an autonomous and non-preplanned way. Such inter...
Pedro Sequeira, Ana Paiva
COLING
2008
14 years 11 months ago
Linguistically Annotated BTG for Statistical Machine Translation
Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into statistical machine translation (SMT). In this paper, we p...
Deyi Xiong, Min Zhang, AiTi Aw, Haizhou Li
TOG
2012
232views Communications» more  TOG 2012»
13 years 5 days ago
Discovery of complex behaviors through contact-invariant optimization
We present a motion synthesis framework capable of producing a wide variety of important human behaviors that have rarely been studied, including getting up from the ground, crawl...
Igor Mordatch, Emanuel Todorov, Zoran Popovic

Publication
240views
13 years 8 months ago
Bayesian multitask inverse reinforcement learning
We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or ...
Christos Dimitrakakis, Constantin A. Rothkopf
EMNLP
2010
14 years 7 months ago
Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation
In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data...
Jordan L. Boyd-Graber, Philip Resnik