Overview of Supervised Learning

By Trevor J. Hastie, Jerome H. Friedman, Robert Tibshirani. 2001

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tom Goldstein
2. Andrew Gordon Wilson
3. Yann LeCun
4. Yuandong Tian
5. Micah Goldblum
6. Yoshua Bengio
7. Ruslan Salakhutdinov
8. Sergey Levine

In Computer Networks and Communications

1. Varun Chandrasekaran
2. Andrew Gordon Wilson
3. Yann LeCun
4. Guy Van den Broeck
5. Vijay S. Pande
6. Tom Goldstein
7. Hamed Pirsiavash
8. Nicolas Papernot

Citations in the corpus

• PotentialNet for Molecular Property Prediction (2018, background): “Second, since the parameters grow exponentially with the number of dimensions, the model suffers from the “curse of dimen-sionality” 4 : while image processing may entail a square 3 2 filter, the corresponding filter for volumetric molecule processing has 3 3 parameters.”
• Unrolling SGD: Understanding Factors Influencing Machine Unlearning (2021, background): “In our work, we focus on supervised learning [17].”
• A Semantic Loss Function for Deep Learning with Symbolic Knowledge (2017, background): “Many of these models take large amounts of fully labeled data for granted, and big data is indispensable for discovering accurate representations (Hastie et al., 2009).”
• A Cookbook of Self-Supervised Learning (2023, background): “Self-supervised learning defines a pretext task based on unlabeled inputs to produce descriptive and intelligible representations [Hastie et al., 2009, Goodfellow et al., 2016].”