AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recurrent Transformers increase computational depth through temporal recurrence, feeding each token's high-level hidden state into the computation of the next. This raises a natural question: is additional computation better spent on more temporal steps or greater physical depth? We investigate this question using Late...
Latent Recurrent Transformer is studied, a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden state from the previous token as recurrent memory for the next token, while retaining one-forward-per-token decoding with 9% latency overhead over the standard Transformer.
The TaSe (Talk in Pieces, See in Whole) framework is introduced with three main contributions: a hierarchical synthetic captioning dataset spanning three tiers from category names to descriptive sentences; the three-component disentanglement module guided by a novel disentanglement loss function, transforms text embedd...
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