Summary of Variational Deep Survival Machines: Survival Regression with Censored Outcomes, by Qinxin Wang et al.
Variational Deep Survival Machines: Survival Regression with Censored Outcomes
by Qinxin Wang, Jiayuan Huang, Junhui Li, Jiaming Liu
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 This paper proposes a novel method for survival regression, which aims to predict the time when an event of interest will take place. The proposed approach estimates the survival function as a mixture of individual parametric distributions in the presence of censoring. Two variants of variational auto-encoder (VAE) are used to generate latent variables for clustering input covariates. The model is trained end-to-end by jointly optimizing the VAE loss and regression loss. Experimental results on the SUPPORT and FLCHAIN datasets show that the proposed method can effectively improve clustering results and achieve competitive scores with previous methods. The superior performance of the model in long-term prediction is also demonstrated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to guess when something bad will happen, like someone dying or a machine breaking down. They come up with a new way to do this that’s better than before. It uses a special kind of computer program called a VAE (short for variational auto-encoder) to help it make predictions. The researchers tested their idea on two big sets of data and found that it worked really well, even when they were trying to predict what would happen in the long run. |
Keywords
» Artificial intelligence » Clustering » Encoder » Regression
