ArticleEntropy (Basel, Switzerland)2024
Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run.Entropy (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
An information bottleneck (IB) enables the acquisition of useful representations from data by retaining necessary information while reducing unnecessary information. In its objective function, the Lagrange multiplier β controls the trade-off between retention and reduction. This study analyzes the Variational Information Bottleneck (VIB), a standard IB method in deep learning, in the settings of regression problems and derives its optimal solution. Based on this analysis, we propose a framework for regression problems that can obtain the optimal solution of the VIB for all β values with a single training run. This is in contrast to conventional methods that require one training run for each β. The optimization performance of this framework is theoretically discussed and experimentally demonstrated. Our approach not only enhances the efficiency of exploring β in regression problems but also deepens the understanding of the IB's behavior and its effects in this setting.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.