Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer

31 January 2022

Papers citing "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer"

7 / 7 papers shown

Title
Deep Kalman Filters Can Filter Blanka Hovart Anastasis Kratsios Yannick Limmer Xuwei Yang 45 1 0 31 Dec 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts Anastasis Kratsios Haitz Sáez de Ocáriz Borde Takashi Furuya Marc T. Law MoE 41 1 0 05 Feb 2024
Generative Ornstein-Uhlenbeck Markets via Geometric Deep Learning Anastasis Kratsios Cody B. Hyndman 13 0 0 17 Feb 2023
Chaotic Hedging with Iterated Integrals and Neural Networks Ariel Neufeld Philipp Schmocker 17 10 0 21 Sep 2022
Small Transformers Compute Universal Metric Embeddings Anastasis Kratsios Valentin Debarnot Ivan Dokmanić 54 11 0 14 Sep 2022
Universal Approximation Under Constraints is Possible with Transformers Anastasis Kratsios Behnoosh Zamanlooy Tianlin Liu Ivan Dokmanić 51 26 0 07 Oct 2021
Optimal Stopping via Randomized Neural Networks Calypso Herrera Florian Krack P. Ruyssen Josef Teichmann 32 37 0 28 Apr 2021