793 citations · 3.2k across the 19 of their papers we have counts for
5 papers · 1 filter
TuneUp: A Simple Improved Training Strategy for Graph Neural Networks
Weihua Hu, Kaidi Cao, Kexin Huang +4
Despite recent advances in Graph Neural Networks (GNNs), their training strategies remain largely under-explored. The conventional training strategy learns over all nodes in the or…
Learning Backward Compatible Embeddings
Weihua Hu, Rajas Bansal, Kaidi Cao +3
Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding tea…
Learning from Complementary Labels
Takashi Ishida, Gang Niu, Weihua Hu +1
Collecting labeled data is costly and thus a critical bottleneck in real-world classification tasks. To mitigate this problem, we propose a novel setting, namely learning from comp…
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Weihua Hu, Takeru Miyato, Seiya Tokui +2
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes cluster…
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Weihua Hu, Gang Niu, Issei Sato +1
Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance…