Dynamic Restart Policies
... distributions over run time. Note that because the summary variables include some quantities that refer to the initial, unreduced problem (such as the initial number of unbound variables), the feature F combines static and dynamic observations. The feature F may be binary-valued, such as whether the ...
... distributions over run time. Note that because the summary variables include some quantities that refer to the initial, unreduced problem (such as the initial number of unbound variables), the feature F combines static and dynamic observations. The feature F may be binary-valued, such as whether the ...
Philosophy and Computing - An Introduction
... sooner or later our computers may go “vocal”, allowing us to talk and listen to our PC. The possibility in itself is not in question, but have you ever tried to give operating instructions orally to someone who cannot see what he is doing? Or to receive instructions via telephone about where to find ...
... sooner or later our computers may go “vocal”, allowing us to talk and listen to our PC. The possibility in itself is not in question, but have you ever tried to give operating instructions orally to someone who cannot see what he is doing? Or to receive instructions via telephone about where to find ...
Improving the Efficiency of Dynamic Programming on Tree
... the respective sub-problems [Niedermeier, 2006]. The general runtime of these algorithms for an instance of size n is f (k) · nO(1) , where f is an arbitrary function of width k of the used tree decomposition. However, experience shows that even decompositions of the same width lead to significant d ...
... the respective sub-problems [Niedermeier, 2006]. The general runtime of these algorithms for an instance of size n is f (k) · nO(1) , where f is an arbitrary function of width k of the used tree decomposition. However, experience shows that even decompositions of the same width lead to significant d ...
On Constrained Optimization Approach to Object
... of their presence and know how to tackle them properly once they appear. The segmentation problem would become one of the image understanding (IU) problems and the many techniques used in IU could be applied. When each of the methods in the above major approaches faces some of the aforementioned dif ...
... of their presence and know how to tackle them properly once they appear. The segmentation problem would become one of the image understanding (IU) problems and the many techniques used in IU could be applied. When each of the methods in the above major approaches faces some of the aforementioned dif ...
Behavioural Domain Knowledge Transfer for Autonomous Agents
... rk being the reward received at step k. The goal of a reinforcement learning agent is to learn an optimal policy π ∗ = arg maxπ R̄π which maximises the total expected return of an MDP, where typically T and R are unknown. Many approaches to learning an optimal policy involve learning the value funct ...
... rk being the reward received at step k. The goal of a reinforcement learning agent is to learn an optimal policy π ∗ = arg maxπ R̄π which maximises the total expected return of an MDP, where typically T and R are unknown. Many approaches to learning an optimal policy involve learning the value funct ...
A New Entity Salience Task with Millions of Training Examples
... Rather than manually annotating a corpus, we automatically generate salience labels for an existing corpus of document/abstract pairs. We derive the labels using the assumption that the salient entities will be mentioned in the abstract, so we identify and align the entities in each text. Given a do ...
... Rather than manually annotating a corpus, we automatically generate salience labels for an existing corpus of document/abstract pairs. We derive the labels using the assumption that the salient entities will be mentioned in the abstract, so we identify and align the entities in each text. Given a do ...
Computing Shapley values manipulating value division schemes and checking core membership in multi-issue domains
... coalition’s value, or pessimistically assuming that the nonmembers will do what minimizes the coalition’s value. (In either case, the members of the coalition act to maximize the coalition’s value.) The optimistic assumption yields stronger stability (in the sense of the core): if a coalition cannot ...
... coalition’s value, or pessimistically assuming that the nonmembers will do what minimizes the coalition’s value. (In either case, the members of the coalition act to maximize the coalition’s value.) The optimistic assumption yields stronger stability (in the sense of the core): if a coalition cannot ...
Selecting the Best Curve Fit in SoftMax Pro 7 Software | Molecular
... Nonlinear regression Nonlinear data are commonly modeled using logistic regression. In this case, the relationship between the measured values and the measurement variable is nonlinear. The goal is also to find those parameter values that minimize the deviations between the measured and the expect ...
... Nonlinear regression Nonlinear data are commonly modeled using logistic regression. In this case, the relationship between the measured values and the measurement variable is nonlinear. The goal is also to find those parameter values that minimize the deviations between the measured and the expect ...
IEEE Transactions on Evolutionary Computation Special Issue on
... scheduling problems. We invite papers representing high quality research which reflects the recent advances of evolutionary computation in scheduling and demonstrates state-of-the-art theory and practice in this area. Despite many successes, significant research challenges remain in order to design ...
... scheduling problems. We invite papers representing high quality research which reflects the recent advances of evolutionary computation in scheduling and demonstrates state-of-the-art theory and practice in this area. Despite many successes, significant research challenges remain in order to design ...
