Summary of Learning Sum Of Diverse Features: Computational Hardness and Efficient Gradient-based Training For Ridge Combinations, by Kazusato Oko et al.
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
by Kazusato Oko, Yujin Song, Taiji Suzuki, Denny Wu
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the computational and sample complexity of learning target functions with additive structure, motivated by classical additive models, representation learning theory, and large-scale pretraining. The study focuses on two-layer neural networks that learn to perform multiple tasks (skills) simultaneously, where each task is localized in distinct parts of the network. The authors prove that a large subset of polynomial functions can be efficiently learned using gradient descent training, with a polynomial statistical and computational complexity that depends on the number of tasks and the information exponent of individual tasks. Additionally, the paper provides statistical query (SQ) lower bounds for both correlational SQ and full SQ algorithms, demonstrating computational hardness results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to learn many new skills at once, like playing multiple musical instruments or understanding different languages. This paper explores how computers can learn these types of complex tasks efficiently. The researchers focus on a special type of computer program called a two-layer neural network that can learn multiple skills simultaneously. They show that certain complex functions can be learned using this type of program, with the complexity increasing as the number of skills grows. This work helps us understand how computers can learn many new things at once and has implications for artificial intelligence and machine learning. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning * Neural network * Pretraining * Representation learning
