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The high performance of the BERT model could also be attributed [citation needed] to the fact that it is bidirectionally trained. This means that BERT, based on the Transformer model architecture, applies its self-attention mechanism to learn information from a text from the left and right side during training, and consequently gains a deep ...
BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...
The use of AI technologies has been explored for use in the diagnosis and prognosis of Alzheimer's disease (AD). For diagnostic purposes, machine learning models have been developed that rely on structural MRI inputs. [75] The input datasets for these models are drawn from databases such as the Alzheimer's Disease Neuroimaging Initiative. [76]
Model-based diagnosis is an example of abductive reasoning using a model of the system. In general, it works as follows: Principle of the model-based diagnosis. We have a model that describes the behaviour of the system (or artefact). The model is an abstraction of the behaviour of the system and can be incomplete.
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. [1] The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes.
More specifically, the signal-detection model, which assumes that memory strength is a graded phenomenon (not a discrete, probabilistic phenomenon) predicts that the ROC will be curvilinear, and because every recognition memory ROC analyzed between 1958 and 1997 was curvilinear, the high-threshold model was abandoned in favor of signal ...
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...