5 citations · 13 across the 8 of their papers we have counts for
9 papers
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms
Haoxiang Wang, Gargi Balasubramaniam, Haozhe Si +2
Domain generalization asks for models trained over a set of training environments to generalize well in unseen test environments. Recently, a series of algorithms such as Invariant…
Structural Re-weighting Improves Graph Domain Adaptation
Shikun Liu, Tianchun Li, Yongbin Feng +4
In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data us…
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang +6
How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state…
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning
Qian Jiang, Changyou Chen, Han Zhao +6
Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly…
Dual-View Selective Instance Segmentation Network for Unstained Live Adherent Cells in Differential Interference Contrast Images
Fei Pan, Yutong Wu, Kangning Cui +9
Despite recent advances in data-independent and deep-learning algorithms, unstained live adherent cell instance segmentation remains a long-standing challenge in cell image process…
Exploring Gradient-based Multi-directional Controls in GANs
Zikun Chen, Ruowei Jiang, Brendan Duke +2
Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent s…