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Learning via Wasserstein-Based High Probability Generalisation Bounds
The authors contributed equally to this work 37th Conference on Neural Information Processing Systems (NeurIPS 2023). Developing upper bounds on the generalisation gap, i.e., generalisation bounds has been a longstanding topic in statistical learning.
Supplementary Material for " Diversifying Spatial-Temporal Perception for Video Domain Generalization " Kun-Y u Lin
Hard Norm Alignment loss (HNA): apply the HNA loss (Eq. HMDB, which demonstrates the effectiveness of our model. First, we drop feature from a specific spatial group. Method UCF HMDB STDN-T -1 59.2 STDN-T -2 58.1 STDN-T -3 59.4 STDN-T -4 58.9 Full STDN 60.2 Second, we drop feature from a space scale. In our main manuscript, we conduct all experiments based on ResNet-50.
Diversifying Spatial-Temporal Perception for Video Domain Generalization Kun-Y u Lin
However, existing video classification models rely on the i.i.d.
Geometric Transformer with Interatomic Positional Encoding Y usong Wang
Nevertheless, it remains elusive that whether the Transformer-based architecture can do molecular modeling as good as equivariant GNNs.
F ABind: Fast and Accurate Protein-Ligand Binding
However, these methods have notable limitations.