Showing stat.MLShow all
2 papers · 1 filter
stat.ML2025
Is In-Context Universality Enough? MLPs are Also Universal In-Context
Anastasis Kratsios, Takashi Furuya
The success of transformers is often linked to their ability to perform in-context learning. Recent work shows that transformers are universal in context, capable of approximating…
stat.ML2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya +1
Mixture-of-Experts (MoEs) can scale up beyond traditional deep learning models by employing a routing strategy in which each input is processed by a single "expert" deep learning m…