The theory of greedy low-rank learning (GLRL) aims to explain the impres...
Robot teleoperation has been studied for the past 70 years and is releva...
Recently, neural networks have proven their impressive ability to solve
...
Deep neural networks are usually initialized with random weights, with
a...
The simultaneous recognition of multiple objects in one image remains a
...
Conventional RGB-D salient object detection methods aim to leverage dept...
Representing deep neural networks (DNNs) in low-precision is a promising...
With the rapid prevalence of mobile devices and the dramatic proliferati...
Compositionality is a basic structural feature of both biological and
ar...
Salient object detection (SOD) is a crucial and preliminary task for man...
Network security has become an area of significant importance more than ...
Training neural networks on large datasets can be accelerated by distrib...
Current conditional functional dependencies (CFDs) discovery algorithms
...