Deep Learning
Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data
Recent works have parameterized the Hamiltonian by machine learning models (e.g., neural networks), allowing Hamiltonian dynamics to be
Space-time Mixing Attention for Video Transformer
This paper is on video recognition using Transformers.
Appendix: Boosting Out-of-distribution Detection with Typical Features A Type I error and type II error in OOD detection
In the literature on OOD detection, the type II error is also denoted by "FPR
Boosting Out-of-distribution Detection with Typical Features
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Traffic forecasting is challenging due to the complex intra-dependencies (i.e., temporal correlations
