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A comprehensive survey on pretrained foundation models: A history from bert to chatgpt
Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …
Contrastive self-supervised learning: review, progress, challenges and future research directions
In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …
flaws, such as its dependency on manual and costly annotations on large datasets and …
Graphgpt: Graph instruction tuning for large language models
Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …
recursive exchanges and aggregations among nodes. To enhance robustness, self …
Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …
applications such as online page/article classification and social recommendation. While …
Graphmae: Self-supervised masked graph autoencoders
Self-supervised learning (SSL) has been extensively explored in recent years. Particularly,
generative SSL has seen emerging success in natural language processing and other …
generative SSL has seen emerging success in natural language processing and other …
Simgrace: A simple framework for graph contrastive learning without data augmentation
Graph contrastive learning (GCL) has emerged as a dominant technique for graph
representation learning which maximizes the mutual information between paired graph …
representation learning which maximizes the mutual information between paired graph …
Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
Pre-training molecular graph representation with 3d geometry
Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …
material discovery. Molecular graphs are typically modeled by their 2D topological …
Graph self-supervised learning: A survey
Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …
works have focused on (semi-) supervised learning, resulting in shortcomings including …
3d infomax improves gnns for molecular property prediction
Molecular property prediction is one of the fastest-growing applications of deep learning with
critical real-world impacts. Although the 3D molecular graph structure is necessary for …
critical real-world impacts. Although the 3D molecular graph structure is necessary for …