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A deep stacking network (DSN) [31] (deep convex network) is based on a hierarchy of blocks of simplified neural network modules. It was introduced in 2011 by Deng and Yu. [32] It formulates the learning as a convex optimization problem with a closed-form solution, emphasizing the mechanism's similarity to stacked generalization. [33]
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [25] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...
Deep learning is a subset of machine learning methods that utilize neural networks for representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
A lithium-ion or Li-ion battery is a type of rechargeable battery that uses the reversible intercalation of Li + ions into electronically conducting solids to store energy. In comparison with other commercial rechargeable batteries, Li-ion batteries are characterized by higher specific energy, higher energy density, higher energy efficiency, a longer cycle life, and a longer calendar life.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset. [18]
A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a predefined loss function on a given data set. [3] The objective function takes a set of ...
October 11, 2024 at 1:08 PM. Residents in Texas have filed a lawsuit against a bitcoin mine, alleging it's too loud. Stephen Lam/Getty Images. Residents near Granbury, Texas, want an 'intolerably ...
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