Št. zadetkov: 103
Jonathan Williams ,
Sophie Wang ,
Mikey Siegel ,
Jun Ki Lee ,
Fardad Faridi ,
Jason B. Alonso ,
Sigurdur Orn Adalgeirsson ,
Thomas Kollar ,
Nicholas Roy ,
Stefanie Tellex ,
Jesse Gray ,
Aditya Undurti ,
Jonathan P. How ,
Sanford T. Freedman ,
Philipp Robbel ,
Julie A. Adams ,
Kenton Williams ,
Paula Aguilera ,
Matt Berlin ,
Cynthia Breazeal
Video in druga učna gradiva
Oznake:
computer science;robotics
The humanoid robot Nexi and a team of robot helicopters are deployed in response to a simulated fire aboard a Navy ship. Working together with a remote human operator, the robots search for survivors and guide them to safety. A mixed-initiative tasking system allows the human operator to specify tea ...
Leto:
2009
Vir:
videolectures.net
Izvirni znanstveni članek
Oznake:
Calvinism;Christianity;Lutheranism;Reformation;Protestantism;Stundism;
This article examines the history and current state of Protestantism in Ukraine. The research explores the causes and consequences of the growth and decline of Protestantism in the country, as well as the development and situation of Protestant communities in modern Ukraine. The study utilizes gener ...
Leto:
2023
Vir:
Teološka fakulteta (UL TEOF)
Video in druga učna gradiva
Oznake:
computer science;machine learning;markov processes
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data. While likelihood-based methods have been extensively explored, to our knowledge, le ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;web mining;link analysis
Leto:
2007
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;visual computing
In this talk, I explore the mathematical relationships between duality in information geometry, convex analysis, and divergence functions.
First, from the fundamental inequality of a convex function, a family of divergence measures can be constructed, which specializes to the familiar Bregman diver ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;reinforcement learning
Distributed reinforcement learning (DRL) has been studied as an approach to learn control policies thorough interactions between distributed agents and environments. The main emphasis of DRL has been put on the way to learn sub-optimal policies with the least or limited sharing of agents' perception ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;semi-supervised learning
Graph transduction methods label input data by learning a classification function that is regularized to exhibit smoothness along a graph over labeled and unlabeled samples. In practice, these algorithms are sensitive to the initial set of labels provided by the user. For instance, classification ac ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;markov processes
Learning sparse Markov networks based on the maximum margin principle remains an open problem in structured prediction. In this paper, we proposed the Laplace max-margin Markov network (LapM3N), and a general class of Bayesian M3N (BM3N) of which the LapM3N is a special case and enjoys a sparse repr ...
Leto:
2008
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
computer science;machine learning;supervised learning;regression
Supervised topic models utilize document’s side information for discovering predictive low dimensional representations of documents;
and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous
and categorical response var ...
Leto:
2009
Vir:
videolectures.net
Video in druga učna gradiva
Oznake:
Sparsity is a desirable property in high dimensional learning. The $\ell_1$-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity; but achieving both in a single structured prediction model remains difficult. This paper presents an $\ell_1$-norm max-margin M ...
Leto:
2009
Vir:
videolectures.net