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UID:20260601T092021EDT-3302VsXmnO@132.216.98.100
DTSTAMP:20260601T132021Z
DESCRIPTION:Title: Block-State Transformers\n\nAbstract: State space models
  (SSMs) have shown impressive results on tasks that require modeling long-
 range dependencies and efficiently scale to long sequences owing to their 
 subquadratic runtime complexity. Originally designed for continuous signal
 s\, SSMs have shown superior performance on a plethora of tasks\, in visio
 n and audio\; however\, SSMs still lag Transformer performance in Language
  Modeling tasks. In this work\, we propose a hybrid layer named Block-Stat
 e Transformer (BST)\, that internally combines an SSM sublayer for long-ra
 nge contextualization\, and a Block Transformer sublayer for short-term re
 presentation of sequences. We study three different\, and completely paral
 lelizable\, variants that integrate SSMs and block-wise attention. We show
  that our model outperforms similar Transformer-based architectures on lan
 guage modeling perplexity and generalizes to longer sequences. In addition
 \, the Block-State Transformer demonstrates more than tenfold increase in 
 speed at the layer level compared to the Block-Recurrent Transformer when 
 model parallelization is employed.\n
DTSTART:20231115T180000Z
DTEND:20231115T190000Z
LOCATION:Room 1214\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jonathan Pilault (MILA) CANCELED
URL:/mathstat/channels/event/jonathan-pilault-mila-can
 celed-352698
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