Search results
Results from the WOW.Com Content Network
Depth of processing falls on a shallow to deep continuum. [citation needed] Shallow processing (e.g., processing based on phonemic and orthographic components) leads to a fragile memory trace that is susceptible to rapid decay. Conversely, deep processing (e.g., semantic processing) results in a more durable memory trace. [1] There are three ...
They claimed that the level of processing information was dependent upon the depth at which the information was being processed; mainly, shallow processing and deep processing. According to Craik and Lockhart, the encoding of sensory information would be considered shallow processing, as it is highly automatic and requires very little focus.
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.
Most dyslexic readers of shallow orthographic systems learn to decode words with relative ease compared to dyslexics using deep orthographies, though they continue to have difficulty with reading fluency and comprehension. [8] The hallmark system of dyslexia in a shallow orthography is a comparatively slow speed of rapid automatized naming.
A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.
Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG , HPSG , LFG , TAG , the Prague School ).
The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.
Many languages allow generic copying by one or either strategy, defining either one copy operation or separate shallow copy and deep copy operations. [1] Note that even shallower is to use a reference to the existing object A, in which case there is no new object, only a new reference. The terminology of shallow copy and deep copy dates to ...