Density networks
By David John Cameron MacKay, Mark N. Gibbs. 2000
29 citations recorded by the source corpus.
Subfields: Probabilistic & Bayesian Methods
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In Probabilistic & Bayesian Methods
- Roger Baker Grosse
- Ruslan Salakhutdinov
- Sebastian Nowozin
- Ryan P. Adams
- Balaji Lakshminarayanan
- Yuri Burda
- Danilo Jimenez Rezende
- Yoshua Bengio
Citations in the corpus
- On the Quantitative Analysis of Decoder-Based Generative Models (2016, methodology): “We refer to this set of models collectively as decoder-based models, also known as density networks (MacKay & Gibbs, 1998).”
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019, methodology): “A variety of methods have been developed for quantifying predictive uncertainty in DNNs. Probabilistic neural networks such as mixture density networks (MacKay & Gibbs, 1999) capture the inherent ambiguity in outputs for a given input, also referred to as aleatoric uncertainty (Kendall & Gal, 2017).”
- High-Dimensional Probability Estimation with Deep Density Models (2013, methodology): “Independent components analysis [Bell and Sejnowski, 1995] and the related idea of a density network [MacKay and Gibbs, 1997] are examples of bijective models that exploit invertible linear transformations; these, however, have rather limited expressiveness.”
- Stick-Breaking Variational Autoencoders (2016, methodology): “A Variational Autoencoder (VAE) is model comprised of two multilayer perceptrons: one acts as a density network (MacKay & Gibbs, 1999) mapping a latent variable z i to an observed datapoint x i , and the other acts as an inference model (Salimans & Knowles, 2013) performing the reverse mapping from…”
- A Unifying Review of Deep and Shallow Anomaly Detection (2020, methodology): “Consequently, many researchers have developed techniques for calibrating the probabilities of classifiers [473], [487]–[492] or Bayesian uncertainty quantification [417], [418], [472], [474], [475], [493].”
- Pattern Recognition and Machine Learning (2006, unknown): “This is exploited in a general latent variable model called a density network (MacKay, 1995; MacKay and Gibbs, 1999) in which the nonlinear function is governed by a multilayered neural network.”