Generalization and similarity in exemplar models of categorization: Insights from machine learning

By Frank Jäkel, Bernhard Schölkopf, Felix Wichmann. 2008

73 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Probabilistic & Bayesian Methods, Statistical Learning Theory

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. Lawrence Carin
2. Dinghan Shen
3. Yizhe Zhang
4. Chunyuan Li
5. Charles Kemp
6. Weizhu Chen
7. Alan Jern
8. Bill Dolan

In Developmental and Educational Psychology

1. Charles Kemp
2. Alan Jern
3. Josh Tenenbaum
4. Michael D. Lee
5. Brenden M. Lake

In Probabilistic & Bayesian Methods

1. Lawrence Carin
2. Charles Kemp
3. Dinghan Shen
4. Weizhu Chen
5. Alan Jern
6. Yizhe Zhang
7. Yu Zhang
8. Qiang Yang

In Statistical Learning Theory

1. Lawrence Carin
2. Weizhu Chen

Citations in the corpus

• What Makes Good In-Context Examples for GPT-3? (2021, background): “It is very related to the exemplar-based learning (Jäkel et al., 2008; Ziyadi et al., 2020).”
• A probabilistic account of exemplar and category generation. (2013, background): “As explained earlier, the GCM is equivalent to a generative model that maintains a probability distribution with peaks at the observed exemplars (Ashby & Alfonso-Reese, 1995; Jäkel et al., 2008).”
• Differential Attention for Visual Question Answering (2018, unknown): “In this model, individuals compare new stimuli with the instances already stored in memory [9][20] and obtain answers based on these exemplars.”