Summary of Deep End-to-end Survival Analysis with Temporal Consistency, by Mariana Vargas Vieyra and Pascal Frossard
Deep End-to-End Survival Analysis with Temporal Consistency
by Mariana Vargas Vieyra, Pascal Frossard
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel Survival Analysis algorithm presented in this study efficiently handles large-scale longitudinal data by drawing inspiration from Reinforcement Learning principles and Temporal Learning concepts. The approach incorporates temporal consistency, a hypothesis that past and future outcomes evolve smoothly over time, to provide a stable training signal that captures long-term relationships. The framework supports arbitrarily complex architectures, enabling the modeling of intricate dependencies, and allows for end-to-end training. Empirical evidence demonstrates its ability to exploit temporal consistency across datasets of varying sizes, outperforming benchmarks on datasets with long sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze data from over time has been developed. This approach helps us understand how things change or stay the same over many steps. It’s like a game where we try to predict what will happen next based on what happened before. The algorithm is good at finding patterns in the data that last for a long time, which can be helpful for tasks like predicting when something will break or how well someone will do in the future. |
Keywords
* Artificial intelligence * Reinforcement learning
