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Another alternative to RLHF called Direct Preference Optimization (DPO) has been proposed to learn human preferences. Like RLHF, it has been applied to align pre-trained large language models using human-generated preference data. Unlike RLHF, however, which first trains a separate intermediate model to understand what good outcomes look like ...
Optimality modeling is the modeling aspect of optimization theory. It allows for the calculation and visualization of the costs and benefits that influence the outcome of a decision, and contributes to an understanding of adaptations. The approach based on optimality models in biology is sometimes called optimality theory. [1]
Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically ...
Direct preference optimization, a technique for aligning AI models with human preferences; Double pushout graph rewriting, in computer science; Other.
Many neuroevolution algorithms have been defined. One common distinction is between algorithms that evolve only the strength of the connection weights for a fixed network topology (sometimes called conventional neuroevolution), and algorithms that evolve both the topology of the network and its weights (called TWEANNs, for Topology and Weight Evolving Artificial Neural Network algorithms).
The optimization of these different foraging and predation strategies can be explained by the optimal foraging theory. In each case, there are costs, benefits, and limitations that ultimately determine the optimal decision rule that the predator should follow.
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 [1] with further developments by Yoon in 1987, [2] and Hwang, Lai and Liu in 1993. [3]
This shift in preference suggests that females discriminate between males through direct observation of cognitively-demanding tasks. [103] Zebra finches: researchers conducted a problem-solving experiment similar to the one described above. [104] However, male problem-solving performance was not found to influence female mating preferences.