Temporal dynamics of a neural solution to the aperature
... has a small RF (is looking for edge) and MT solves because it’s neurons/RF are bigger MT response should be tuned for actual direction of motion and not for orientation of the contour (not in actual direction of the motion) ...
... has a small RF (is looking for edge) and MT solves because it’s neurons/RF are bigger MT response should be tuned for actual direction of motion and not for orientation of the contour (not in actual direction of the motion) ...
[Powerpoint version].
... While artificial intelligence aims to make "machines do things that would require intelligence if done by humans", metacreation is a new field devoted to endow machines with creative behavior. The presentation will propose an overview of past and current works on computational creativty conducted at ...
... While artificial intelligence aims to make "machines do things that would require intelligence if done by humans", metacreation is a new field devoted to endow machines with creative behavior. The presentation will propose an overview of past and current works on computational creativty conducted at ...
Finite-time Analysis of the Multiarmed Bandit Problem*
... that 0 < d < 1). Note also that this is a result stronger than those of Theorems 1 and 2, as it establishes a bound on the instantaneous regret. However, unlike Theorems 1 and 2, here we need to know a lower bound d on the difference between the reward expectations of the best and the second best ma ...
... that 0 < d < 1). Note also that this is a result stronger than those of Theorems 1 and 2, as it establishes a bound on the instantaneous regret. However, unlike Theorems 1 and 2, here we need to know a lower bound d on the difference between the reward expectations of the best and the second best ma ...
Chapter 1 Powerpoints - People Server at UNCW
... 8. The use of meta-level knowledge to effect more sophisticated control of problem solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential area of research. ...
... 8. The use of meta-level knowledge to effect more sophisticated control of problem solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential area of research. ...
Multi-armed bandit
In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a gambler at a row of slot machines (sometimes known as ""one-armed bandits"") has to decide which machines to play, how many times to play each machine and in which order to play them. When played, each machine provides a random reward from a distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in ""some aspects of the sequential design of experiments"".A theorem, the Gittins index published first by John C. Gittins gives an optimal policy in the Markov setting for maximizing the expected discounted reward.In practice, multi-armed bandits have been used to model the problem of managing research projects in a large organization, like a science foundation or a pharmaceutical company. Given a fixed budget, the problem is to allocate resources among the competing projects, whose properties are only partially known at the time of allocation, but which may become better understood as time passes.In early versions of the multi-armed bandit problem, the gambler has no initial knowledge about the machines. The crucial tradeoff the gambler faces at each trial is between ""exploitation"" of the machine that has the highest expected payoff and ""exploration"" to get more information about the expected payoffs of the other machines. The trade-off between exploration and exploitation is also faced in reinforcement learning.