Search results
Results from the WOW.Com Content Network
Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. [1] DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. [1]DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, [2] with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, [3] whereas the ...
Nvidia advertised DLSS as a key feature of the GeForce 20 series cards when they launched in September 2018. [4] At that time, the results were limited to a few video games, namely Battlefield V, [5] or Metro Exodus, because the algorithm had to be trained specifically on each game on which it was applied and the results were usually not as good as simple resolution upscaling.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
PEPA (Performance Evaluation Process Algebra) is a stochastic process algebra designed for modeling computer and communication systems introduced by Jane Hillston in the 1990s. The language extends classical process algebras such as Milner's CCS and Hoare's CSP by introducing probabilistic branching and timing of transitions.
Analytical Performance Modeling is a method to model the behaviour of a system in a spreadsheet. It is used in Software performance testing . It allows evaluation of design options and system sizing based on actual or anticipated business usage.
Evaluation measures for an information retrieval (IR) system assess how well an index, search engine, or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms.
The CIPP model of evaluation was developed by Daniel Stufflebeam and colleagues in the 1960s.CIPP is an acronym for Context, Input, Process and Product. CIPP is an evaluation model that requires the evaluation of context, input, process and product in judging a programme's value.
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).