5 accepted papers
Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, have recently been recognized as potential alternatives to softmax attention thanks to their linear complexity and competitive performance. However, although their linear-memory advantage during traini…
Hybrid dynamical systems are prevalent in science and engineering to express complex systems with continuous and discrete states. To learn laws of systems, all previous methods for equation discovery in hybrid systems follow a two-stage paradigm, i.e. they first group time series into small cluster…
Dynamical systems with complex behaviours, e.g. immune system cells interacting with a pathogen, are commonly modelled by splitting the behaviour in different regimes, or *modes*, each with simpler dynamics, and then learn the switching behaviour from one mode to another. To achieve this, Switching…
Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. Most research considers learning representations from single images, ignoring any potential interactions between…
Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects a…