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  2. Encoding specificity principle - Wikipedia

    en.wikipedia.org/wiki/Encoding_specificity_principle

    The encoding specificity principle is the general principle that matching the encoding contexts of information at recall assists in the retrieval of episodic memories.It provides a framework for understanding how the conditions present while encoding information relate to memory and recall of that information.

  3. Encoding (memory) - Wikipedia

    en.wikipedia.org/wiki/Encoding_(memory)

    This allows data to be conveyed in the short term, without consolidating anything for permanent storage. From here a memory or an association may be chosen to become a long-term memory, or forgotten as the synaptic connections eventually weaken. The switch from short to long-term is the same concerning both implicit memory and explicit memory.

  4. Recall (memory) - Wikipedia

    en.wikipedia.org/wiki/Recall_(memory)

    The recency effect occurs when the short-term memory is used to remember the most recent items, and the primacy effect occurs when the long-term memory has encoded the earlier items. The recency effect can be eliminated if there is a period of interference between the input and the output of information extending longer than the holding time of ...

  5. Encoding/decoding model of communication - Wikipedia

    en.wikipedia.org/wiki/Encoding/decoding_model_of...

    In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.

  6. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    The T5 encoder can be used as a text encoder, much like BERT. It encodes a text into a sequence of real-number vectors, which can be used for downstream applications. For example, Google Imagen [ 26 ] uses T5-XXL as text encoder, and the encoded text vectors are used as conditioning on a diffusion model .

  7. Source–message–channel–receiver model of communication

    en.wikipedia.org/wiki/Source–message–channel...

    The channel is the means used to send the message. The receiver is the audience for whom the message is intended. They have to decode it to understand it. [4] [30] Despite the emphasis on only four basic components, Berlo initially identifies a total of six components. The two additional components are encoder and decoder. [31]

  8. Context-dependent memory - Wikipedia

    en.wikipedia.org/wiki/Context-dependent_memory

    In psychology, context-dependent memory is the improved recall of specific episodes or information when the context present at encoding and retrieval are the same. In a simpler manner, "when events are represented in memory, contextual information is stored along with memory targets; the context can therefore cue memories containing that contextual information". [1]

  9. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    Both encoder and decoder can use self-attention, but with subtle differences. For encoder self-attention, we can start with a simple encoder without self-attention, such as an "embedding layer", which simply converts each input word into a vector by a fixed lookup table .