Due to the lack of rotation invariance in traditional convolution operations, even acting a
slight rotation on the input can severely degrade the performance of Convolutional Neural …

Periodic material or crystal property prediction using machine learning has grown popular in
recent years as it provides a computationally efficient replacement for classical simulation …

For many applications in image analysis, learning models that are invariant to translations
and rotations is paramount. This is the case, for example, in medical imaging where the …

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been
actively studied. However, how to quickly teach a robot to grasp a novel target object in …

The label-free single cell analysis by machine and Deep Learning, in combination with
digital holography in transmission microscope configuration, is becoming a powerful …

A key step in computational pathology is to automate the laborious process of manual nuclei
segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite …

Predicting the evolution of a representative sample of a material with microstructure is a
fundamental problem in homogenization. In this work we propose a graph convolutional …

Integrating deep learning for crop monitoring presents opportunities and challenges,
particularly in object detection under varying environmental conditions. This study …

Humans and animals recognize objects irrespective of the beholder's point of view, which
may drastically change their appearances. Artificial pattern recognizers also strive to …

In networks, a link prediction task aims at learning potential relations between nodes to
predict unknown potential linkage states. At present, most link prediction methods are used …