The handbook of brain theory and neural networks

By Michael A. Arbib. 1995

Subfields: Model Architectures, Neuroscience

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Citations in the corpus

The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (2018, methodology): “Following RNMT, convolutional neural network based approaches (LeCun and Bengio, 1998) to NMT have recently drawn research attention due to their ability to fully parallelize training to”
Deep Learning for Stock Market Prediction (2020, background): “The Equation 1 shows the relationship between nodes and weights, and biases [29].”
The challenges of joint attention (2006, unknown): “…as central elements of Piaget's developmental psychology but the term has also been used in neurology, cognitive psychology and motor control ((Arbib, 2003) p.36–40) and related notions appeared in artificial intelligence under names like frames or scripts (Minsky, 1975, Schank and Abelson,…”
Imitative and Direct Learning as Interacting Factors in Life History Evolution (2017, unknown): “Artificial neural network models that learn from representative streams of input-output samples have already provided good accounts of many forms of behavior in humans and other animals [1].”
How Many Data Samples is an Additional Instruction Worth? (2022, unknown): “Large-scale benchmarks such as Imagenet (Rus-sakovsky et al., 2015), SQuAD (Rajpurkar et al., 2018) and architectural development in models such as CNNs (Amari et al., 2003) and transformers (Vaswani et al., 2017) have propelled our progress in deep learning.”
Review of tool condition monitoring in machining and opportunities for deep learning (2020, methodology): “The reinforcement learning method consists of five basic elements [76, 77].”