Conformalized Quantum DeepONet Ensembles reduce operator inference from quadratic to linear complexity using QOrthoNNs and SPQCs while delivering distribution-free uncertainty guarantees through ensemble conformal prediction.
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a first-class problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel strategies, such as parameter sharing (through a new family of models we call TreeNets) as well as training under ensemble-aware and diversity-encouraging losses. We demonstrate that TreeNets can improve ensemble performance and that diverse ensembles can be trained end-to-end under a unified loss, achieving significantly higher "oracle" accuracies than classical ensembles.
citation-role summary
method 1
citation-polarity summary
verdicts
UNVERDICTED 4roles
method 1polarities
use method 1representative citing papers
Deep learning on information-rich scientific images collapses to one-dimensional predictions due to a mismatch between data priors and the model's simplicity bias, even after robustification techniques.
Extends DAE theory to POMDPs with minimal changes and introduces discrete latent dynamics to cut computational cost, with ALE experiments showing scalability and retained sample efficiency.
A multi-task network is introduced to generate narrow predictive intervals for counts in medical images while maintaining target coverage, tested on cell and white matter hyperintensity counting.
citing papers explorer
-
Anatomy of a failure: When, how, and why deep vision fails in scientific domains
Deep learning on information-rich scientific images collapses to one-dimensional predictions due to a mismatch between data priors and the model's simplicity bias, even after robustification techniques.