Summary of B’mojo: Hybrid State Space Realizations Of Foundation Models with Eidetic and Fading Memory, by Luca Zancato et al.
B’MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
by Luca Zancato, Arjun Seshadri, Yonatan Dukler, Aditya Golatkar, Yantao Shen, Benjamin Bowman, Matthew Trager, Alessandro Achille, Stefano Soatto
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 family of architectures called B’MOJO that enables transductive inference by allowing memory to grow within finite but unknown bounds. Unlike current architectures that use either eidetic or fading memory, B’MOJO combines both seamlessly using Stochastic Realization Theory. The architecture can access different types of memory, including short-term, permanent, fading, and long-term memory, through asynchronously updated retrieval. The authors demonstrate that Transformers, existing State Space Models (SSMs), and hybrid architectures are special cases of B’MOJO. They test B’MOJO on transductive inference tasks such as associative recall, achieving better performance than existing SSMs and Hybrid models. In ordinary language modeling, B’MOJO achieves comparable perplexity to similarly-sized Transformers and SSMs while being faster to train. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way of using computer memory to help machines make decisions. Right now, most computers use either a little bit of memory or a lot of memory, but not both at the same time. The new method, called B’MOJO, lets computers use as much memory as they need while still being efficient. This means that computers can learn and remember more things than before. The authors tested their method on some tasks and found that it worked better than other methods in certain situations. |
Keywords
» Artificial intelligence » Inference » Perplexity » Recall
