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Geometric deep learning on molecular representations
Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …
and process symmetry information. GDL bears promise for molecular modelling applications …
Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
Perceiver io: A general architecture for structured inputs & outputs
A central goal of machine learning is the development of systems that can solve many
problems in as many data domains as possible. Current architectures, however, cannot be …
problems in as many data domains as possible. Current architectures, however, cannot be …
Segmenter: Transformer for semantic segmentation
Image segmentation is often ambiguous at the level of individual image patches and
requires contextual information to reach label consensus. In this paper we introduce …
requires contextual information to reach label consensus. In this paper we introduce …
Segvit: Semantic segmentation with plain vision transformers
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …
propose the SegViT. Previous ViT-based segmentation networks usually learn a pixel-level …
Prediction of Alzheimer's Disease Using DHO‐Based Pretrained CNN Model
Detecting Alzheimer's disease (AD) early on allows patients to take preventative measures
before the onset of irreversible brain damage, which is a critical factor in the treatment of …
before the onset of irreversible brain damage, which is a critical factor in the treatment of …
Object-contextual representations for semantic segmentation
In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …
A brief survey on semantic segmentation with deep learning
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Object detection in optical remote sensing images: A survey and a new benchmark
Substantial efforts have been devoted more recently to presenting various methods for
object detection in optical remote sensing images. However, the current survey of datasets …
object detection in optical remote sensing images. However, the current survey of datasets …
Machine learning in materials science
Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …
method and the density functional theory (DFT)‐based method, are unable to keep pace …