PulseAugur
EN
LIVE 18:05:36
ENTITY MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

PulseAugur coverage of MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers — every cluster mentioning MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers across labs, papers, and developer communities, ranked by signal.

Show in brief
Total · 30d
1
1 over 90d
Releases · 30d
0
0 over 90d
Papers · 30d
1
1 over 90d
TIER MIX · 90D
RECENT · PAGE 1/1 · 1 TOTAL
  1. RESEARCH · CL_15913 ·

    Researchers explore weight decay, in-context learning, and acceleration for Transformer models

    Researchers have developed several new methods to improve the efficiency and theoretical understanding of Transformer models. One paper provides a functional-analytic characterization of weight decay, demonstrating its …