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which enhanced the output of Perceiver to accommodate more than just classification.long-context autoregressive model.
the same team of Andrew Jaegle and colleagues that built Perceiver.The original Perceiver in fact brought improved efficiency over Transformers by performing attention on a latent representation of input.DeepMind and Google Brains Perceiver AR architecture reduces the task of computing the combinatorial nature of inputs and outputs into a latent space.
Experts in the field say computing tasks are destined to get bigger and biggest because scale matters.but rather allows the network to learn which long-context inputs to attend to and propagate through the network.
writing that Perceiver AR is a good candidate for a general-purpose.
Perceiver AR can learn to perfectly recognize long-context patterns over distances of at least 100k tokens on a synthetic copy task.and the ability to hold long meetings with up to 100 participants.
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