13 questions · 7 sources · 0 syntheses
Deep Learning and Neural Networks
Learning with multilayer artificial neural networks.
Source-derived questions
Questions extracted from the papers in this topic, excluding the curated questions above.
- How can deep neural network models serve as testable, end-to-end trainable implementations of cognitive theories?
- How did the AlexNet breakthrough at the 2012 ImageNet competition change the field's attention to deep learning?
- In what ways can insights from deep learning models inform the design of instructional and learning technology systems?
- What did Minsky and Papert's book Perceptrons prove about single-layer neural networks?
- What implications does cross-species evidence for the spacing effect have for designing curriculum learning schedules in machine learning?
- What is a Boltzmann machine, and how did it extend Hopfield's physical intuition about neural networks?
- What specific contributions did Hinton, LeCun, and Bengio each make to deep learning before it became mainstream?
- Why did Perceptrons cool enthusiasm for connectionist AI approaches for a decade?
- Why did a machine-learning approach succeed where decades of molecular dynamics simulation had struggled?
- Why did backpropagation applied to multi-layer neural networks become the technique that made deep learning work at scale?
- Why is Valiant's 1984 framework still relevant to modern neural networks?
- Why might spaced presentation of training examples improve generalization in artificial neural networks, not just retention?