292 citations · 770 across the 46 of their papers we have counts for
4 papers · 1 filter
Generalization Bounds for Transformer-Based Next-Token Prediction in a Language Model
Insung Kong, Niklas Dexheimer, Johannes Schmidt-Hieber
A refined statistical understanding of LLM pre-training requires the analysis of the transformer architecture for data distributions that encapsulate key characteristics of text da…
Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber
We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networ…
Central limit theorems for the outputs of fully convolutional neural networks with time series input
Annika Betken, Giorgio Micali, Johannes Schmidt-Hieber
Deep learning is widely deployed for time series learning tasks such as classification and forecasting. Despite the empirical successes, only little theory has been developed so fa…
Semi-Supervised Learning on Graphs using Graph Neural Networks
Juntong Chen, Claire Donnat, Olga Klopp +1
Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this ga…