Summary of Cross-entropy Is All You Need to Invert the Data Generating Process, by Patrik Reizinger et al.
Cross-Entropy Is All You Need To Invert the Data Generating Process
by Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E. Vogt, Randall Balestriero, Wieland Brendel, David Klindt
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 delves into the mystery of supervised learning’s effectiveness, exploring how models learn interpretable factors of variation in a linear fashion. Building on empirical phenomena like neural analogy-making and the linear representation hypothesis, researchers have made progress in self-supervised learning using nonlinear Independent Component Analysis (ICA). The authors extend these identifiability results to parametric instance discrimination, demonstrating that even in standard classification tasks, models learn representations of ground-truth factors of variation up to a linear transformation. Empirical studies on simulated data and the DisLib benchmark validate the theoretical contribution. Finally, experiments on ImageNet show that trained models encode representations allowing linear decoding of proxy factors of variation. This work provides significant insights into the unreasonable effectiveness of supervised deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to figure out why supervised learning is so good at recognizing patterns. It looks at how models learn and shows that they can learn things in a simple, easy-to-understand way. The researchers also test their ideas on different kinds of data and show that it works really well. They even look at pictures and find that the models are able to understand what’s happening in those pictures by learning from them. |
Keywords
» Artificial intelligence » Classification » Deep learning » Self supervised » Supervised
