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Works451 from public data
- Adaptive Graph Convolutional Neural Networks880
A generalized and flexible graph CNN taking data of arbitrary graph structure as input is proposed, in that way a task-driven adaptive graph is learned for each graph data while training.
- Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks863
A novel bi-directional graph model is proposed, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics of rumors by operating on both top-down and bottom-up propagation of rumors.
- Transformer-based unsupervised contrastive learning for histopathological image classification840
This work proposes a novel SSL strategy called semantically-relevant contrastive learning (SRCL), which compares relevance between instances to mine more positive pairs and aligns multiple positive instances with similar visual concepts, which increases the diversity of positives and results in more informative representations.
- Graph Representation Learning via Graphical Mutual Information Maximization706
An unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder is developed, which outperforms state-of-the-art unsuper supervised counterparts, and even sometimes exceeds the performance of supervised ones.
- scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data682
A pretrained deep neural network-based model, single-cell bidirectional encoder representations from transformers (scBERT), to overcome the challenges of existing annotation methods, and is validated on cell type annotation, novel cell type discovery, robustness to batch effects and model interpretability.
- Large-Scale Multi-View Spectral Clustering via Bipartite Graph650
A novel large-scale multi-view spectral clustering approach based on the bipartite graph that uses local manifold fusion to integrate heterogeneous features and can be easily extended to handle the out-of-sample problem.
- Discrimination-aware channel pruning for deep neural networks642
This work investigates a simple-yet-effective method, called discrimination-aware channel pruning, to choose those channels that really contribute to discriminative power and proposes a greedy algorithm to conduct channel selection and parameter optimization in an iterative way.
- 607
- Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks580
This work proposes Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL pooling to efficiently learn imaging features from the WSI and then aggregate WSI-level information to patient-level.
- Learning with structured sparsity571
This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing by allowing arbitrary structures on the feature set, which generalizes the group sparsity idea.
- SMILES-BERT559
The proposed semi-supervised model named SMILES-BERT, which consists of attention mechanism based Transformer Layer outperforms the state-of-the-art methods on all three datasets, showing the effectiveness of the unsupervised pre-training and great generalization capability of the pre-trained model.
- Graph Convolutional Networks for Temporal Action Localization549
This paper builds an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge and applies the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
- 532
- The benefit of group sparsity521
The result shows that group Lasso is superior to standard Lasso for strongly group-sparse signals, and provides a convincing theoretical justification for using group sparse regularization when the underlying group structure is consistent with the data.
- Progressive Feature Alignment for Unsupervised Domain Adaptation490
The Progressive Feature Alignment Network (PFAN) is proposed to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain.
- Vision-Language Pre-Training with Triple Contrastive Learning411
TCL is the first work that takes into account local structure information for multi-modality representation learning and achieves the new state of the art on various common downstream vision-language tasks such as image-text retrieval and visual question answering.
- 382
- Efficient MR image reconstruction for compressed MR imaging307
An efficient algorithm for MR image reconstruction that minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization is proposed.
- 292
- Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection288
This paper proposes semantic-aware neural networks to extract the semantic information of the binary code, and finds that the order of the CFG's nodes is important for graph similarity detection, so it is adopted convolutional neural network (CNN) on adjacency matrices to Extract the order information.
- MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm287
The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values, and it is believed that MolA ICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket.
- Early triage of critically ill COVID-19 patients using deep learning277
A deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission and is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness.
- Towards robust and effective shape modeling: Sparse shape composition266
A novel Sparse Shape Composition model (SSC), formulated as a sparse learning problem, that can be solved by an efficient expectation-maximization (EM) type of framework and extensively validated on two medical applications.
- Adversarial Sparse Transformer for Time Series Forecasting265
Adversarial Sparse Transformer (AST) is proposed, a new time series forecasting model based on Generative Adversarial Networks (GANs), based on the ground-truth provided during training and replaced by the network’s own one-step ahead output during inference during inference.
- Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer261
The pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort and lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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