How deeply does Sutskever's List cover end-to-end learning?

Explains the approach conceptually through a landmark result rather than detailing architecture, optimization, or training mechanics.

End-to-end learning

As explained in Sutskever's List

End-to-end training is a training approach that learns a complete mapping from raw inputs to outputs without separately engineered intermediate representations.

End-to-end learning trains a complete mapping from raw inputs to outputs, allowing the model to learn intermediate representations instead of relying on separately engineered features. Its breakthrough in computer vision came when AlexNet learned directly from raw images and overturned decades of handcrafted approaches. section 1.1

AlexNet and ImageNet

In September 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton trained a convolutional neural network for ImageNet classification. AlexNet dramatically reduced error rates in the ILSVRC, providing compelling evidence that end-to-end neural training could outperform established vision pipelines. section 1.1

How Sutskever's List covers it

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Also explained in

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Optimization Algorithms: AI techniques for design, planning, and control problemsA learning approach that trains a model to map inputs directly to desired outputs with minimal separately engineered intermediate stages.

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Related concepts

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objective functionuses · An objective function assigns values to candidate solutions, predictions, or parameter settings according to a criterion to be minimized or maximized.
Retrieveruses · A retriever is a software component that searches or queries an information source and returns items relevant to a query for use by downstream processes.
modularitycontrasts with · Modularity is the property of a system being organized into separable components with distinct responsibilities and defined interactions.

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