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CIFAR-100 Dataset Like CIFAR-10, above, but 100 classes of objects are given. Classes labelled, training set splits created. 60,000 Images Classification 2009 [18] [36] A. Krizhevsky et al. CINIC-10 Dataset A unified contribution of CIFAR-10 and Imagenet with 10 classes, and 3 splits. Larger than CIFAR-10.
"The Taskmaster corpus consists of THREE datasets, Taskmaster-1 (TM-1), Taskmaster-2 (TM-2), and Taskmaster-3 (TM-3), comprising over 55,000 spoken and written task-oriented dialogs in over a dozen domains." [338] Taskmaster-1: goal-oriented conversational dataset. It includes 13,215 task-based dialogs comprising six domains.
These figures go down to 38.27%, 4.96% and 2.22% under the "intermediate" SSP2-4.5 scenario, and to 22.65%, 2.03% and 1.15% under the high-mitigation SSP1-2.6. [ 62 ] In 2020, a study in Nature Climate Change estimated the effects of Arctic sea ice decline on polar bear populations (which rely on the sea ice to hunt seals ) under two climate ...
Images may appear in more than one class. The dataset was motivated by non-parametric models of neural activations in the visual cortex upon seeing images. [1] [2] The CIFAR-10 dataset uses a subset of the images in this dataset, but with independently generated labels, as the original labels were not reliable. The CIFAR-10 set has 6000 ...
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]
A percentage change is a way to express a change in a variable. It represents the relative change between the old value and the new one. [6]For example, if a house is worth $100,000 today and the year after its value goes up to $110,000, the percentage change of its value can be expressed as = = %.
[1] [5] Compared to other datasets, the Pile's main distinguishing features are that it is a curated selection of data chosen by researchers at EleutherAI to contain information they thought language models should learn and that it is the only such dataset that is thoroughly documented by the researchers who developed it.
An example of the first resample might look like this X 1 * = x 2, x 1, x 10, x 10, x 3, x 4, x 6, x 7, x 1, x 9. There are some duplicates since a bootstrap resample comes from sampling with replacement from the data. Also the number of data points in a bootstrap resample is equal to the number of data points in our original observations.