3 citations · 5 across the 5 of their papers we have counts for
5 papers
Implicit neural representation for change detection
Peter Naylor, Diego Di Carlo, Arianna Traviglia +2
Identifying changes in a pair of 3D aerial LiDAR point clouds, obtained during two distinct time periods over the same geographic region presents a significant challenge due to the…
Structural Explanations for Graph Neural Networks using HSIC
Ayato Toyokuni, Makoto Yamada
Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learn…
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
Yuki Takezawa, Han Bao, Kenta Niwa +2
SGD with momentum is one of the key components for improving the performance of neural networks. For decentralized learning, a straightforward approach using momentum is Distribute…
Scale dependant layer for self-supervised nuclei encoding
Peter Naylor, Yao-Hung Hubert Tsai, Marick Laé +1
Recent developments in self-supervised learning give us the possibility to further reduce human intervention in multi-step pipelines where the focus evolves around particular objec…
Deep learning-based quality filtering of mechanically exfoliated 2D crystals
Yu Saito, Kento Shin, Kei Terayama1 +7
Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensiona…