Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
- Published
- Source
- arXiv
- Paper number
- 368
- Field
- Machine Learning
- arXiv ID
- 2606.06624
Key points
- It presents data compression as a unifying principle behind all representation learning methods, including PCA, denoising, score matching, and information gain maximization.
- It mathematically interprets ResNet, CNN, and Transformer architectures as iterative optimization algorithms and clarifies the principles behind architecture design.
- It proves that the encoder-decoder structure is a necessary design for ensuring the accuracy and consistency of representations through a closed-loop transcription framework.
- In v2.0, it adds the theoretical justification for contrastive learning and DINO, derives a causal CRATE transformer, and expands the relationship between distribution learning and representation learning.
- It demonstrates the practical value of these theoretical principles step by step in large-scale real-world applications involving visual, motion, and text data.
Paper links
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