Summary of Amortized Variational Inference For Deep Gaussian Processes, by Qiuxian Meng and Yongyou Zhang
Amortized Variational Inference for Deep Gaussian Processes
by Qiuxian Meng, Yongyou Zhang
First submitted to arxiv on: 18 Sep 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 The paper introduces a new approach to deep Gaussian processes (DGPs) called amortized variational inference, which learns an inference function that maps each observation to variational parameters. This allows for more expressive priors and fewer input-dependent inducing variables, leading to improved performance at reduced computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This method helps complex functions become more manageable by learning from smaller inputs. It’s a game-changer in the field of Gaussian processes! |
Keywords
» Artificial intelligence » Inference
