Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals

Ähnliche Einträge

• Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
  von: Limmer, Yannick, et al.
  Veröffentlicht: (2024)
• Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
  von: Hong, Ruiyang, et al.
  Veröffentlicht: (2024)
• Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
  von: Kratsios, Anastasis
  Veröffentlicht: (2026)
• Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data
  von: Kratsios, Anastasis, et al.
  Veröffentlicht: (2025)
• Efficient Approximation to Analytic and $L^p$ functions by Height-Augmented ReLU Networks
  von: Li, ZeYu, et al.
  Veröffentlicht: (2026)