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Found 5 papers, 3 papers with code
The Impact of Geometric Complexity on Neural Collapse in Transfer Learning
Many of the recent remarkable advances in computer vision and language models can be attributed to the success of transfer learning via the pre-training of large foundation models.
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)
