Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks

Documenti analoghi

• A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
  di: Yang, Sikun, et al.
  Pubblicazione: (2023)
• Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
  di: Yang, Sikun, et al.
  Pubblicazione: (2024)
• Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
  di: Chen, Cheng, et al.
  Pubblicazione: (2020)
• Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
  di: Yu, Xincan, et al.
  Pubblicazione: (2024)
• On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
  di: Liang, Ling, et al.
  Pubblicazione: (2024)