The goal of continual learning is to provide intelligent agents that are...
We present a framework for learning disentangled representation of CapsN...
Continual learning aims to rapidly and continually learn the current tas...
graph neural networks (GNNs) are the dominant paradigm for modeling and
...
Continual learning aims to learn a sequence of tasks by leveraging the
k...
In this paper, we study the optimistic online convex optimization proble...
Real world datasets often contain noisy labels, and learning from such
d...
Recent years have witnessed enormous progress of online learning. Howeve...
The extensive research leveraging RGB-D information has been exploited i...
Online learning is an important technical means for sketching massive
re...
Over recent decades have witnessed considerable progress in whether
mult...
Multi-view learning is a learning problem that utilizes the various
repr...
Multi-view learning accomplishes the task objectives of classification b...
Recent years have witnessed growing interests in online incremental lear...
Multi-view learning can cover all features of data samples more
comprehe...
Multi-view learning attempts to generate a model with a better performan...
Asynchronous events sequences are widely distributed in the natural worl...
In recent years, mining the knowledge from asynchronous sequences by Haw...
In this paper, we use the Hawkes process to model the sequence of failur...
Abstract. Most of the real world data we encounter are asynchronous even...
In this paper, we propose a novel Attentive Multi-View Deep Subspace Net...