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AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
Generative adversarial networks in medical image augmentation: a review
Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …
image-based diagnosis and treatment models is increasing. Generative Adversarial …
A survey on oversmoothing in graph neural networks
Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …
increase of the network depth. This effect is known as over-smoothing, which we …
Pointnext: Revisiting pointnet++ with improved training and scaling strategies
PointNet++ is one of the most influential neural architectures for point cloud understanding.
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
Vision gnn: An image is worth graph of nodes
Network architecture plays a key role in the deep learning-based computer vision system.
The widely-used convolutional neural network and transformer treat the image as a grid or …
The widely-used convolutional neural network and transformer treat the image as a grid or …
On over-squashing in message passing neural networks: The impact of width, depth, and topology
Abstract Message Passing Neural Networks (MPNNs) are instances of Graph Neural
Networks that leverage the graph to send messages over the edges. This inductive bias …
Networks that leverage the graph to send messages over the edges. This inductive bias …
Structure-aware transformer for graph representation learning
The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …
Rethinking network design and local geometry in point cloud: A simple residual MLP framework
Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …