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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
In RAII, holding a resource is a class invariant, and is tied to object lifetime. Resource allocation (or acquisition) is done during object creation (specifically initialization), by the constructor, while resource deallocation (release) is done during object destruction (specifically finalization), by the destructor. In other words, resource ...
In this case, player allocates higher weight to the actions that had a better outcome and choose his strategy relying on these weights. In machine learning, Littlestone applied the earliest form of the multiplicative weights update rule in his famous winnow algorithm, which is similar to Minsky and Papert's earlier perceptron learning algorithm ...
The problem often arises in resource allocation where the decision-makers have to choose from a set of non-divisible projects or tasks under a fixed budget or time constraint, respectively. The knapsack problem has been studied for more than a century, with early works dating as far back as 1897. [1]
The ratio for two agents and two resources improved from 0.828 to 5/6 ≈ 0.833 with a complete-allocation mechanism, and strictly more than 5/6 with a partial-allocation mechanism. The upper bound improved from 0.841 to 5/6+ε for a complete-allocation mechanism, and to 0.8644 for a partial mechanism.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
A performance analysis of a continuous ant colony algorithm with respect to its various parameters (edge selection strategy, distance measure metric, and pheromone evaporation rate) showed that its performance and rate of convergence are sensitive to the chosen parameter values, and especially to the value of the pheromone evaporation rate. [33]
Dynamic Resource Allocation in Healthcare: A Bayesian OCBA framework has been applied to allocate resources in hospital emergency departments, balancing service quality with operational efficiency. By minimizing expected opportunity costs, this approach supports real-time decision-making in high-stakes environments. [ 22 ]