Summary of State-free Inference Of State-space Models: the Transfer Function Approach, by Rom N. Parnichkun et al.
State-Free Inference of State-Space Models: The Transfer Function Approach
by Rom N. Parnichkun, Stefano Massaroli, Alessandro Moro, Jimmy T.H. Smith, Ramin Hasani, Mathias Lechner, Qi An, Christopher Ré, Hajime Asama, Stefano Ermon, Taiji Suzuki, Atsushi Yamashita, Michael Poli
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 presents a novel approach to designing state-space models for deep learning applications, leveraging their dual representation as transfer functions. The proposed algorithm, which we term state-free inference, bypasses the need for explicit memory or computational costs associated with increasing state sizes. Our key innovation lies in parametrizing frequency domain transfer functions, enabling direct computation of convolutional kernels’ spectra via a single Fast Fourier Transform. Experimental results on multiple sequence lengths and state sizes demonstrate an average 35% training speed improvement over S4 layers, while delivering state-of-the-art downstream performances compared to attention-free approaches. Notably, we also achieve improved perplexity in language modeling over a long convolutional Hyena baseline by introducing our transfer function parametrization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way to make computers learn and process data faster. This paper shows how to design special models that can do this efficiently. The idea is to look at these models from a different perspective, using something called “transfer functions.” This allows us to skip some of the extra steps needed in other approaches, making it much faster. In tests, this new method was 35% faster than others and did just as well or better on certain tasks. It’s like finding a shortcut that makes things run smoother! |
Keywords
» Artificial intelligence » Attention » Deep learning » Inference » Perplexity
