Summary of Parallelizing Autoregressive Generation with Variational State Space Models, by Gaspard Lambrechts et al.
Parallelizing Autoregressive Generation with Variational State Space Models
by Gaspard Lambrechts, Yann Claes, Pierre Geurts, Damien Ernst
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to autoregressive sequence modeling using variational state space models (VSSMs). VSSMs are based on recurrent models like state space models (SSMs) and attention-based models such as Transformers. The key innovation is that both the encoder and decoder in the VSSM are SSMs, enabling parallel training and generation. This allows for faster processing times compared to traditional autoregressive methods. Additionally, the VSSM can be conditioned on partial sequence realizations, which is useful in language generation tasks. Experimental results demonstrate that the VSSM achieves similar quality to Transformer models while speeding up processing time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to make computers generate sequences of information quickly and accurately. Right now, some special kinds of computer models can do this well, but they have limitations. The researchers created a new kind of model called the variational state space model (VSSM) that can both learn from data and generate new sequence information in parallel. This means it’s much faster than other methods. They also showed that their VSSM can be used to generate language sequences, like text or speech, while still being as good as more complex models. |
Keywords
* Artificial intelligence * Attention * Autoregressive * Decoder * Encoder * Transformer
