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Wu et al. recently published a paper on Memorizing Transformers (transformers with states/memory), which extends their perceptive field to unbounded contexts (https://www.youtube.com/watch?v=5AoOpFFjW28&list=PL0NRmB0fnLJQJ3fuIk3yVULtm6_JnQ_zI, https://arxiv.org/abs/2203.08913). I am curious to hear what you think about how S4/Sashimi might compare with this new transformer model. My hunch is that S4 might be theoretically similar if you use the exponential measure density.
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This model seems somewhat different from S4 in spirit. The memorization mechanism seems more similar to the line of work on memory augmented neural networks (MANN), where the memory mechanisms are based on heuristic memory banks. In comparison, S4's mechanism has a precise mathematical interpretation of function reconstruction. I do think that S4's mechanism might not be sufficient for all settings and some form of memory augmentation could be useful.
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Hi,
Wu et al. recently published a paper on Memorizing Transformers (transformers with states/memory), which extends their perceptive field to unbounded contexts (https://www.youtube.com/watch?v=5AoOpFFjW28&list=PL0NRmB0fnLJQJ3fuIk3yVULtm6_JnQ_zI, https://arxiv.org/abs/2203.08913). I am curious to hear what you think about how S4/Sashimi might compare with this new transformer model. My hunch is that S4 might be theoretically similar if you use the exponential measure density.
The text was updated successfully, but these errors were encountered: