Considerable research efforts have been devoted to LiDAR-based 3D object...
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesi...
Multi-task regression attempts to exploit the task similarity in order t...
The demand of probabilistic time series forecasting has been recently ra...
Existing neural architecture search (NAS) methods often return an
archit...
An important step in the task of neural network design, such as
hyper-pa...
Recent work on efficient neural network architectures focuses on discove...
For a learning task, Gaussian process (GP) is interested in learning the...
Convolutional operations have two limitations: (1) do not explicitly mod...
Deep kernel learning (DKL) leverages the connection between Gaussian pro...
Modern crowd counting methods usually employ deep neural networks (DNN) ...
We propose a method to incrementally learn an embedding space over the d...
We present a developmental framework based on a long-term memory and
rea...
Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has
r...
We propose to use the concept of the Hamming bound to derive the optimal...
The state diagrams of a class of singular linear feedback shift register...
In this paper, a framework of video face replacement is proposed and it ...
Deep CNN-based object detection systems have achieved remarkable success...
Domain adaptation (DA) aims to generalize a learning model across traini...
Domain adaptation (DA) is transfer learning which aims to leverage label...
Domain adaptation is transfer learning which aims to generalize a learni...
Hashing is one of the most popular and powerful approximate nearest neig...
Measuring visual similarity is critical for image understanding. But wha...