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cs.LG2021
Universal Approximation Under Constraints is Possible with Transformers
Anastasis Kratsios, Behnoosh Zamanlooy, Tianlin Liu +1
Many practical problems need the output of a machine learning model to satisfy a set of constraints, . Nevertheless, there is no known guarantee that classical neural network ar…
cs.LG2021
Universal Regular Conditional Distributions
Anastasis Kratsios
We introduce a deep learning model that can universally approximate regular conditional distributions (RCDs). The proposed model operates in three phases: first, it linearizes inpu…
cs.LG2021★ 11 cited
Universal Approximation Theorems for Differentiable Geometric Deep Learning
Anastasis Kratsios, Leonie Papon
This paper addresses the growing need to process non-Euclidean data, by introducing a geometric deep learning (GDL) framework for building universal feedforward-type models compati…