Summary of Posterior Approximation Using Stochastic Gradient Ascent with Adaptive Stepsize, by Kart-leong Lim et al.
Posterior Approximation using Stochastic Gradient Ascent with Adaptive Stepsize
by Kart-Leong Lim, Xudong Jiang
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Scalable Bayesian nonparametrics like Dirichlet process mixtures can now be applied to larger datasets at a reduced computational cost, thanks to recent algorithms. The stochastic variational inference method performs well but relies on closed-form solutions. This paper explores the use of stochastic gradient ascent (SGA) as an alternative for posterior approximation in Dirichlet process mixture models. SGA is widely used in deep neural network training and can be optimized using stepsize techniques like momentum methods. By introducing Fisher information, this approach enables adaptive stepsize optimization for efficient posterior approximation. Experimental results demonstrate that our method maintains performance while reducing computational cost when compared to traditional closed-form coordinate ascent learning on several datasets, including Caltech256 and SUN397. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to analyze huge amounts of data quickly without sacrificing accuracy. This paper shows how to make it possible by using a new algorithm called stochastic gradient ascent (SGA). SGA is already used for training deep neural networks but can also be used for something called Bayesian nonparametrics. The researchers in this paper found that by optimizing the stepsize of the SGA algorithm, they could make it even faster and more efficient. They tested their method on several large datasets and showed that it works just as well as a more complicated approach while being much quicker. |
Keywords
» Artificial intelligence » Inference » Neural network » Optimization
