Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, et al.
NeurIPS 2023
In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, as it requires to re-evaluate published algorithms and their different configurations in order to find a close to optimal classifier. To facilitate this process, before biasing our decision toward a class of neural networks or running an expensive search over the network space, we propose to estimate the classification difficulty of the dataset. Our method computes a single number that characterizes the dataset difficulty 97 × faster than training state-of-the-art networks. The proposed method can be used in combination with network topology and hyper-parameter search optimizers to efficiently drive the search toward promising neural network configurations.
Haoyu Han, Xiaorui Liu, et al.
NeurIPS 2023
Giovanni Mariani, Andreea Anghel, et al.
Int. J. Parallel Program
Dhaval Patel, Giridhar Ganapavarapu, et al.
AAAI 2022
Quoc Tran-Dinh, Nhan H. Pham, et al.
Mathematical Programming