Summary of On the Connection Between Noise-contrastive Estimation and Contrastive Divergence, by Amanda Olmin et al.
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
by Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore noise-contrastive estimation (NCE), a technique used to estimate unnormalised probabilistic models like energy-based models. These models are effective at capturing complex data distributions. Unlike traditional maximum likelihood (ML) methods that rely on importance sampling or Markov chain Monte Carlo (MCMC), NCE uses a proxy criterion to avoid the need for calculating a normalization constant, which is often intractable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Noise-contrastive estimation helps us understand complex patterns in data by creating models without needing to calculate a tricky part called a normalization constant. This is different from how we usually learn models using important sampling or computer simulations that can take a long time. By using NCE, researchers can make more accurate predictions about where new data points will fit into our model. |
Keywords
* Artificial intelligence * Likelihood
