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Semantic encoding is the processing and encoding of sensory input that has particular meaning or can be applied to a context. Various strategies can be applied such as chunking and mnemonics to aid in encoding, and in some cases, allow deep processing, and optimizing retrieval.
Memory is a site of storage and enables the retrieval and encoding of information, which is essential for the process of learning. [2] Learning is dependent on memory processes because previously stored knowledge functions as a framework in which newly learned information can be linked. [5]
Elaborative encoding is a mnemonic system that uses some form of elaboration, such as an emotional cue, to assist in the retention of memories and knowledge. [1] In this system one attaches an additional piece of information to a memory task which makes it easier to recall.
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.
State-dependent memory or state-dependent learning is the phenomenon ... Therefore putting oneself in the same mindset as one experienced at the time of encoding will ...
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
Transfer-appropriate processing (TAP) is a type of state-dependent memory specifically showing that memory performance is not only determined by the depth of processing (where associating meaning with information strengthens the memory; see levels-of-processing effect), but by the relationship between how information is initially encoded and how it is later retrieved.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.