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ml:gradient_clipping [2022/05/29 22:11] – [Papers] jmflanigml:gradient_clipping [2023/06/15 07:36] (current) – external edit 127.0.0.1
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 ===== Papers ===== ===== Papers =====
   * [[https://arxiv.org/pdf/1905.11881.pdf|Zhang et al 2020 - Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity]]   * [[https://arxiv.org/pdf/1905.11881.pdf|Zhang et al 2020 - Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity]]
-  * An extreme form of gradient clipping, where everything gets clipped, is the "Manhattan-Learning rule.  Rprop is an advancement over this, see the {{papers:rprop_paper.pdf|Rprop paper}}.+  * An extreme form of gradient clipping, where everything gets clipped, is the "Manhattan-Learning rule" (see the {{papers:rprop_paper.pdf|Rprop paper}}).  Rprop is an advancement over this.
  
 ===== Blog Posts ===== ===== Blog Posts =====
ml/gradient_clipping.1653862274.txt.gz · Last modified: 2023/06/15 07:36 (external edit)

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