Generative model (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Generative model" in English language version.

refsWebsite

Global rank English rank

4stanford.edu

179^th place

183^rd place

3cmu.edu

1,564^th place

1,028^th place

3openai.com

1,559^th place

1,155^th place

3arxiv.org

69^th place

59^th place

2springer.com

274^th place

309^th place

1books.google.com

3^rd place

1microsoft.com

153^rd place

151^st place

arxiv.org

Kaplan, Jared; McCandlish, Sam; Henighan, Tom; Brown, Tom B.; Chess, Benjamin; Child, Rewon; Gray, Scott; Radford, Alec; Wu, Jeffrey; Amodei, Dario (2020). "Scaling Laws for Neural Language Models". arXiv:2001.08361 [stat.ML].
Brock, Andrew; Donahue, Jeff; Simonyan, Karen (2018). "Large Scale GAN Training for High Fidelity Natural Image Synthesis". arXiv:1809.11096 [cs.LG].
Razavi, Ali; van den Oord, Aaron; Vinyals, Oriol (2019). "Generating Diverse High-Fidelity Images with VQ-VAE-2". arXiv:1906.00446 [cs.LG].

books.google.com

Bishop, C. M.; Lasserre, J. (24 September 2007), "Generative or Discriminative? getting the best of both worlds", in Bernardo, J. M. (ed.), Bayesian statistics 8: proceedings of the eighth Valencia International Meeting, June 2-6, 2006, Oxford University Press, pp. 3–23, ISBN 978-0-19-921465-5

cmu.edu

cs.cmu.edu

Three leading sources, Ng & Jordan 2002, Jebara 2004, and Mitchell 2015, give different divisions and definitions. Ng, Andrew Y.; Jordan, Michael I. (2002). "On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes" (PDF). Advances in Neural Information Processing Systems. Jebara, Tony (2004). Machine Learning: Discriminative and Generative. The Springer International Series in Engineering and Computer Science. Kluwer Academic (Springer). ISBN 978-1-4020-7647-3. Mitchell, Tom M. (2015). "3. Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression" (PDF). Machine Learning.
Mitchell 2015: "We can use Bayes rule as the basis for designing learning algorithms (function approximators), as follows: Given that we wish to learn some target function $f\colon X\to Y$ , or equivalently, $P(Y\mid X)$ , we use the training data to learn estimates of $P(X\mid Y)$ and $P(Y)$ . New X examples can then be classified using these estimated probability distributions, plus Bayes rule. This type of classifier is called a generative classifier, because we can view the distribution $P(X\mid Y)$ as describing how to generate random instances X conditioned on the target attribute Y. Mitchell, Tom M. (2015). "3. Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression" (PDF). Machine Learning.
Mitchell 2015: "Logistic Regression is a function approximation algorithm that uses training data to directly estimate $P(Y\mid X)$ , in contrast to Naive Bayes. In this sense, Logistic Regression is often referred to as a discriminative classifier because we can view the distribution $P(Y\mid X)$ as directly discriminating the value of the target value Y for any given instance X Mitchell, Tom M. (2015). "3. Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression" (PDF). Machine Learning.