Summary of Representation Learning with Conditional Information Flow Maximization, by Dou Hu et al.
Representation Learning with Conditional Information Flow Maximization
by Dou Hu, Lingwei Wei, Wei Zhou, Songlin Hu
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposed conditional information flow maximization framework extracts noise-invariant sufficient representations from input data and target tasks to enhance pre-trained language models’ generalization. This method learns representations with good feature uniformity and predictive ability by maximizing input-representation mutual information, unlike the information bottleneck approach that avoids over-compression. To mitigate redundant features, a conditional information minimization principle eliminates negative effects while preserving noise-invariant features. Experiments on 13 language understanding benchmarks demonstrate improved performance for classification and regression tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to improve computer models that talk like humans. It’s about making these models better at learning from data and using what they learned to make good predictions. The scientists came up with a new way to represent information, which is important because it lets the models work well even when they’re given noisy or incomplete data. They tested this method on 13 different tasks and found that it made the models more accurate and reliable. |
Keywords
» Artificial intelligence » Classification » Generalization » Language understanding » Regression
