266 citations · 981 across the 59 of their papers we have counts for
7 papers · 1 filter
Topological Deep Learning: A Review of an Emerging Paradigm
Ali Zia, Abdelwahed Khamis, James Nichols +3
Topological data analysis (TDA) provides insight into data shape. The summaries obtained by these methods are principled global descriptions of multi-dimensional data whilst exhibi…
Towards a Robust Differentiable Architecture Search under Label Noise
Christian Simon, Piotr Koniusz, Lars Petersson +2
Neural Architecture Search (NAS) is the game changer in designing robust neural architectures. Architectures designed by NAS outperform or compete with the best manual network desi…
Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
Jieming Zhou, Tong Zhang, Pengfei Fang +2
Prior to the introduction of Graph Neural Networks (GNNs), modeling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The co…
A Survey on Graph-Based Deep Learning for Computational Histopathology
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman +2
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysi…
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman +2
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and spec…
Contextually Plausible and Diverse 3D Human Motion Prediction
Sadegh Aliakbarian, Fatemeh Sadat Saleh, Lars Petersson +2
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular…
Learning Variations in Human Motion via Mix-and-Match Perturbation
Mohammad Sadegh Aliakbarian, Fatemeh Sadat Saleh, Mathieu Salzmann +3
Human motion prediction is a stochastic process: Given an observed sequence of poses, multiple future motions are plausible. Existing approaches to modeling this stochasticity typi…