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Found 4 papers, 3 papers with code
Agnostic Learning with Multiple Objectives
We further implement the algorithm in a popular symbolic gradient computation framework and empirically demonstrate on a number of datasets the benefits of $\almo$ framework versus learning with a fixed mixture weights distribution.
Towards Task and Architecture-Independent Generalization Gap Predictors
We also show results for architecture-independent, task-independent, and out-of-distribution generalization gap prediction tasks.
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention.
Improving Neural Architecture Search Image Classifiers via Ensemble Learning
However, once a good building block is found, manual design is still required to assemble the final architecture as a combination of multiple blocks under a predefined parameter budget constraint.
Ranked #63 on
Image Classification
on CIFAR-100
(using extra training data)
