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Embedded software is computer software, written to control machines or devices that are not typically thought of as computers, commonly known as embedded systems. It is typically specialized for the particular hardware that it runs on and has time and memory constraints. [1] This term is sometimes used interchangeably with firmware. [2]
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
An embedded system on a plug-in card with processor, memory, power supply, and external interfaces. An embedded system is a specialized computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system.
An embedded system on a plug-in card, featuring a processor, memory, power supply, and various external interfaces. An Embedded Operating System (EOS) is an operating system designed specifically for embedded computer systems. These systems aim to enhance functionality and reliability to perform dedicated tasks. [1]
In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold. An immersion is precisely a local embedding , i.e. for any point x ∈ M {\displaystyle x\in M} there is a neighborhood x ∈ U ⊂ M {\displaystyle x\in U\subset M} such that f : U → N {\displaystyle f ...
Embedded system, a special-purpose system in which the computer is completely encapsulated by the device it controls; Embedding, installing media into a text document to form a compound document <embed></embed>, a HyperText Markup Language (HTML) element that inserts a non-standard object into the HTML document
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In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.