Summary of Learning Linear Attention in Polynomial Time, by Morris Yau et al.
Learning Linear Attention in Polynomial Time
by Morris Yau, Ekin Akyürek, Jiayuan Mao, Joshua B. Tenenbaum, Stefanie Jegelka, Jacob Andreas
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Data Structures and Algorithms (cs.DS)
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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 A novel study provides the first polynomial-time learnability results for single-layer Transformers with linear attention, addressing an open question in computational expressivity. The research shows that linear attention can be viewed as a linear predictor in a suitably defined reproducing kernel Hilbert space (RKHS), enabling conversion of the learning problem into a familiar ordinary linear prediction task. Additionally, the study demonstrates how to efficiently identify training datasets that guarantee correct generalization across all inputs for learned models. Polynomial-time learnability is achieved for various computations expressible via linear attention, including associative memories, finite automata, and Universal Turing Machines (UTMs) with polynomially bounded computation histories. Empirical validation is provided through experiments on learning random linear attention networks, key-value associations, and executing finite automata. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study helps machines learn from data how to simulate different types of computers, like those that use Boolean logic or Turing machines. The researchers show that a type of AI model called the Transformer can be taught quickly and accurately to perform these simulations. They also find ways to ensure that the learned models generalize well to new inputs. This means that even if we only have limited training data, the models will still work correctly for other types of inputs. The study shows that certain types of computations are efficiently learnable by Transformers, including associative memories and finite automata. |
Keywords
» Artificial intelligence » Attention » Generalization » Transformer
