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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.
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- 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.
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- 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.
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- 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.
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- 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.
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- RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval256
A Retrieval with Clustering-guided Contrastive Learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance.
- Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization232
This work proposes a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power and dynamic group sparsity (DGS) is utilized in this algorithm.
- End-to-End Learning of Motion Representation for Video Understanding229
This work proposes TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data, which achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.
- Graph CNN for Survival Analysis on Whole Slide Pathological Images216
This work proposes to model WSI as graph and then develop a graph convolutional neural network (graph CNN) with attention learning that better serves the survival prediction by rendering the optimal graph representations of WSIs.
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- Weakly Supervised Region Proposal Network and Object Detection205
This paper proposes a weakly supervised region proposal network which is trained using only image-level annotations and achieves the state-of-the-art performance for WSOD with performance gain of about \(3\%\) on average.
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- Graph Few-Shot Learning via Knowledge Transfer196
This work innovatively proposes a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph.
- Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis195
This work innovatively proposes a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way and theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
- Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis189
Detailed morphological features of tumor pathological images predict prognosis in patients with lung cancer, and statistical models based on these extracted features stratified NSCLC patients into high‐risk and low‐risk groups.
- TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation188
This paper first comprehensively investigates the performance of ViT on a variety of domain adaptation tasks, and proposes an unified framework, namely Transferable Vision Transformer (TVT), to fully exploit the transferability ofViT for domain adaptation.
- A Restricted Black-Box Adversarial Framework Towards Attacking Graph Embedding Models180
This paper investigates the theoretical connections between graph signal processing and graph embedding models in a principled way and forms a generalized adversarial attacker, GF-Attack, which is constructed by the graph filter and feature matrix and validated on several benchmark datasets.
- Seq2seq Fingerprint179
A novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification, which is also robust and task-insensitive.
- Discrimination-aware Network Pruning for Deep Model Compression178
This paper proposes a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power of the network, and proposes a greedy algorithm to solve the resultant problem.
- Semi-Supervised Graph Classification: A Hierarchical Graph Perspective172
This work proposes a novel supervised, self-attentive graph embedding method called SAGE, which embeds graph instances of arbitrary size into fixed-length vectors and demonstrates that SEAL-C/AI not only outperform competing methods by a significant margin in terms of accuracy/Macro-F1, but also generate meaningful interpretations of the learned representations.
- Transfer Learning via Learning to Transfer.169
A novel transfer learning framework known as Learning to Transfer (L2T) is proposed to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences and empirically demonstrate its superiority over several state-of-the-art transfer learning algorithms.
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- Weakly supervised mitosis detection in breast histopathology images using concentric loss168
An automatic and accurate system for detecting mitosis in histopathology images using a deep segmentation network to produce segmentation map and a novel concentric loss function is proposed to train the semantic segmentsation network on weakly supervised mitosis data.
- Collaborative Channel Pruning for Deep Networks168
Empirical evaluation indicates that the proposed CCP algorithm achieves higher classification accuracy with similar computational complexity than other state-of-the-art channel pruning algorithms.
- Fast multi-contrast MRI reconstruction166
An efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data is proposed and the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework.
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- Application advances of deep learning methods for de novo drug design and molecular dynamics simulation154
Two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective and IML is introduced for the deep learning model interpretability of de noVO drug design and MD simulations.
- RetroXpert: Decompose Retrosynthesis Prediction like A Chemist152
A novel template-free algorithm for automatic retrosynthetic expansion inspired by how chemists approach Retrosynthesis prediction is devised, outperforming the state-of-the-art baselines by a significant margin and provides chemically reasonable interpretation.
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- Structure of human steroid 5α-reductase 2 with the anti-androgen drug finasteride145
A crystal structure of human SRD5A2 is reported, revealing a unique 7-TM structural topology and an intermediate adduct of finasteride and NADPH as NADP-dihydrofinasteride in a largely enclosed binding cavity inside the transmembrane domain that regulates NADPH/NADP+ exchange.
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- Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays138
A weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer and max-min pooling for classifying common thoracic diseases as well as localizing suspicious lesion regions on chest X-rays is proposed.
- Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning132
A novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations, while a new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
- Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation120
This work presents an innovative framework, designed to mitigate the image translation bias and align cross-domain features with the same category by performing the target-to-source translation and reconstructing both source and target images from their predicted labels.
- Compressive Sensing MRI with Wavelet Tree Sparsity120
This paper proposes a fast convex optimization algorithm that outperforms the state-of-the-art CS-MRI algorithms, and gain better reconstructions results on real MR images than general tree based solvers or algorithms.
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- Deep Correlational Learning for Survival Prediction from Multi-modality Data102
To remove the view discrepancy, the proposed DeepCorrSurv first explicitly maximizes the correlation among the views, then it transfers feature hierarchies from view commonality and specifically fine-tunes on the survival regression task.
- Background Subtraction Using Low Rank and Group Sparsity Constraints102
A unified and robust framework to effectively handle diverse types of videos, e.g., videos from stationary or moving cameras, is proposed using both low rank and group sparsity constraints.
- Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods98
This work paves the way for applying unpaired deep learning methods to enhance LDCT images without requiring aligned FD and low-dose images from the same patient.
- Composite splitting algorithms for convex optimization98
The proposed composite splitting algorithms are applied to the compressed MR image reconstruction and low-rank tensor completion and demonstrate the superior performance of the proposed algorithms in terms of both accuracy and computation complexity.
- Towards Fast Adaptation of Neural Architectures with Meta Learning96
A novel Transferable Neural Architecture Search method based on meta-learning, which learns a meta-architecture that is able to adapt to a new task quickly through a few gradient steps, which makes the transferred architecture suitable for the specific task.
- Graph Convolutional Module for Temporal Action Localization in Videos93
It is claimed that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it to enhance action localization.
- DTWNet: a Dynamic Time Warping Network89
For the first time, theDTW loss is theoretically analyzed, and a stochastic backpropogation scheme is proposed to improve the accuracy and efficiency of the DTW learning.
- Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media88
A fake news detection model named Post-User Interaction Network (PSIN) is proposed, which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics.
- Deep Multi-instance Learning for Survival Prediction from Whole Slide Images87
This work takes advantages of a deep multiple instance learning to encode all possible patterns from WSIs and considers the joint effects from different patterns for clinical outcomes prediction to improve the prediction performances compared with existing state-of-the-arts survival analysis approaches.
- An Efficient Approach to Informative Feature Extraction from Multimodal Data84
This paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities that prevents the "hard" whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation.
- DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations81
DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets.
- Cross-dependent graph neural networks for molecular property prediction81
This work explores the multi-view modeling with graph neural network to form a novel paralleled framework which considers both atoms and bonds equally important when learning molecular representations, and proposes a cross-dependent message passing scheme to enhance information communication of different views.
- Recognizing Predictive Substructures With Subgraph Information Bottleneck81
A novel subgraph information bottleneck (SIB) framework to recognize a predictive yet compressed subgraph to get rid of the noise and redundancy and obtain the interpretable part of the graph and theoretically prove the error bound of the estimation scheme for mutual information and the noise-invariant nature of IB-subgraph.
- Adaptive cost-sensitive online classification73
A family of cost-sensitive online classification algorithms with adaptive regularization with sketching technique is proposed, which significantly accelerates the computational speed with quite slight performance loss and applies to several online anomaly detection tasks from real world.
- An efficient algorithm for dynamic MRI using low-rank and total variation regularizations73
An efficient algorithm for dynamic magnetic resonance (MR) image reconstruction is proposed by solving a primal‐dual form of the original problem and it is theoretically proved that the proposed algorithm achieves a convergence rate of Symbol for N iterations.
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- From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification70
A novel Deep Microscopy Adaptation Network (DMAN) is proposed, which can classify MSIs effectively even without MSI annotations, by reducing domain discrepancies via adversarial learning and entropy minimization, and alleviating class imbalance with sample reweighting.
- Imaging Biomarker Discovery for Lung Cancer Survival Prediction70
Development of a computational approach in H&E stained pathological images to quantitatively describe cellular heterogeneity from different types of cells and demonstrated that survival models bsuilt from the clinical imaging biomarkers have better prediction power than state-of-the-art methods using molecular profiling data and traditional Imaging biomarkers.
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- A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images67
The proposed FMDet algorithm is externally validated on four independent datasets for mitosis detection, which exhibits state-of-the-art performance in comparison with previously published results, and has the potential to be deployed as an assistant decision support tool in clinical practice.
- Query driven-graph neural networks for community search66
Graph Neural Network (GNN) models for both CS and ACS problems are proposed, i.e., Query Driven-GNN (QD- GNN) and Attributed Query Dri driven GNN (AQD -GNN), which combine the local query-dependent structure and global graph embedding and model vertex attributes as a bipartite graph.
- On Self-Distilling Graph Neural Network65
This paper proposes the first teacher-free knowledge distillation method for GNNs, termed GNN Self-Distillation (GNN-SD), that serves as a drop-in replacement of the standard training process and proposes the adaptive discrepancy retaining (ADR) regularizer to empower the transferability of knowledge that maintains high neighborhood discrepancy across GNN layers.
- Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging65
A Graph Attention Multi-instance Learning (Graph Attention MIL) with texture features, which encodes a spatial structure between patches and jointly predicts the TNM staging, improves the performance over the existing state-of-the-art methods and indicates the future research towards graph based learning forTNM staging.
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- Subtype Cell Detection with an Accelerated Deep Convolution Neural Network64
This paper proposes a novel subtype cell detection method with an accelerated deep convolution neural network that not only detects cells but also gives sub type cell classification for the detected cells.
- Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study63
An annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides is aimed at developing an annotation-free Deep learning-enabled artificial intelligence method to predict gene mutations from routinely acquired histological slides.
- Context-Aware Domain Adaptation in Semantic Segmentation61
This paper proposes a cross-attention mechanism based on self-att attention to capture context dependencies between two domains and adapt transferable context and designs two cross-domain attention modules to adapt context dependencies from both spatial and channel views.
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- Exploiting the wavelet structure in compressed sensing MRI60
A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data and shows significant improvement compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.
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- Segment Any Cell: A SAM-Based Auto-Prompting Fine-Tuning Framework for Nuclei Segmentation57
SAC is introduced, an innovative framework that enhances SAM specifically for nuclei segmentation and introduces an innovative auto-prompt generator that produces effective prompts to guide segmentation, a critical factor in handling the complexities of nuclei segmentation in biomedical imaging.
- Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis57
A novel multi-level attention graph neural network (MLA-GNN) designed to explore the gene modules and inter-gene relational information contained in the omics data and achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomicData from COVID-19/non-COVID- 19 patient sera.
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- Calibrationless Parallel MRI with Joint Total Variation Regularization55
Experiments on pMRI datasets demonstrate that the proposed calibrationless method outperforms the state-of-the-art pMRI methods even when they can achieve sufficient calibration data, and far better than existing calibrationless pMRI algorithms.
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- The benefit of tree sparsity in accelerated MRI54
Two algorithms with convex relaxation to solve the tree-based MRI problem are proposed based on FISTA and NESTA frameworks respectively to validate the benefit of tree sparsity in accelerated MRI, both of which have very fast convergence rate and low computational cost.
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- Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer52
Neighbor-Interaction based CTR prediction (NI-CTR) is proposed, which considers this task under a Heterogeneous Information Network (HIN) setting and outperforms state-of-the-art CTR models significantly, and is deployed on the WeChat Official Account Platform with billions of users.
- Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study52
The consistency and accuracy of HER2 assessment by the AI-assisted microscope were significantly improved over those obtained using a conventional microscope and online WSI, and the precision of immunohistochemistry (IHC) 3 + and 2 + scoring was improved while ensuring the recall of fluorescent in situ hybridization (FISH)–positive results in IHC 2+.
- Online Adaptive Asymmetric Active Learning for Budgeted Imbalanced Data52
A novel Online Adaptive Asymmetric Active (OA3) learning algorithm is proposed, based on a new asymmetric strategy (merging both the asymmetric losses and queries strategies), and second-order optimization, which is theoretically analyzed and empirically evaluated on four real-world online anomaly detection tasks.
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- Blood–brain barrier penetration prediction enhanced by uncertainty estimation48
The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations.
- Deformable Segmentation via Sparse Shape Representation48
A novel deformable model is proposed for robust segmentation in the presence of weak/misleading appearance cues and achieves better performance in both studies.
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- Deep Graph Learning46
This tutorial aims to provide a comprehensive introduction to deep graph learning and introduces the applications of DGL towards various domains, including but not limited to drug discovery, computer vision, medical image analysis, social network analysis, natural language processing and recommendation.
- Towards Accurate and Compact Architectures via Neural Architecture Transformer45
A Neural Architecture Transformer (NAT) method is proposed which casts the optimization problem into a Markov Decision Process (MDP) and seeks to replace the redundant operations with more efficient operations, such as skip or null connection.
- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos45
A deep neural network model utilizing both spatial and temporal information from surgical videos for surgical tool presence detection is proposed, and the proposed approach achieves better performance than all state-of-the-art methods.
- Scalable histopathological image analysis via supervised hashing with multiple features45
This work exploits joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework to bridge the semantic gap between low-level features and high-level diagnosis in histopathological image analysis methods.
- Fast iteratively reweighted least squares algorithms for analysis-based sparse reconstruction44
A novel algorithm based on the iterative reweighted least squares (IRLS) framework, and is accelerated by the preconditioned conjugate gradient method that achieves superior performance over 14 state‐of‐the‐art algorithms in terms of both accuracy and computational cost.
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- Seq3seq Fingerprint43
This paper proposes an end-to-end deep learning framework, named as seq3seq fingerprint, that automatically learns a strong representation of each molecule in an unsupervised way from a huge training data pool containing a mixture of both unlabeled and labeled molecules.
- WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm42
WADDAICA is a useful and effective tool to help researchers to modify or design novel drugs by deep learning models and classical algorithms and is considered to have the best scoring power.
- Automatic extraction of cell nuclei from H&E-stained histopathological images42
Experimental results show that the proposed method can lead to promising results in terms of segmentation accuracy and separation of touching nuclei in the pathological images of the tumor tissues from The Cancer Genome Atlas study.
- Deep Implicit Statistical Shape Models for 3D Medical Image Delineation41
A novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP) is introduced that can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior.
- Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding41
This paper proposes an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile, and proposes a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems.
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- How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies40
A deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance.
- Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring39
A Pathologist-Tree Network (PTree-Net) is proposed to sparsely model the whole-slide image (WSI) of immunohistochemistry test of biopsy tissue efficiently in multi-scale manner and outperforms state-of-the-art by a large margin.
- Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation39
This work investigates the posterior collapse problem of the current widely-used RNN-based VAEs for the molecule sequence generation and proposes an effective and efficient solution that achieves the state-of-the-art reconstruction accuracy and competitive validity score on the ZINC 250K dataset.
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- RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction38
This work proposes an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates and can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates.
- A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors38
A graph-based Variational Autoencoder with Gaussian Mixture hidden space with deep learning approach for controllable magnitude of scaffold hopping in generative chemistry can effectively and accurately generate molecules from a given reference compound, with excellent scaffold novelty against known molecules in the literature or patents.
- Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury38
This work proposes a property augmentation strategy to include massive training data with other property information to alleviate the data scarcity problem and significantly outperforms all existing baselines on DILI dataset.
- Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation38
A novel neural network architecture is designed that can incorporate the user input into its skip connections and several improvements to the adversarial training method for the neural network are proposed.
- Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation36
This work comprehensively evaluates the robustness of existing UDA methods and proposes a robust UDA approach that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space and is resistant to adversarial attacks.
- Hierarchical Graph Capsule Network36
This paper innovatively proposes hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies and demonstrates the effectiveness of HGCN and the contribution of each component.
- Understanding Medical Conversations with Scattered Keyword Attention and Weak Supervision from Responses36
This work approaches the medical slot filling as a multi-label classification problem with label-embedding attentive model to pay more attention to scattered medical keywords and learn the classification models by weak-supervision from responses.
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- Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization34
A novel compressive sensing model for dynamic MR reconstruction with total variation (TV) and nuclear norm (NN) regularization that can utilize both spatial and temporal redundancy in dynamic MR images is proposed.
- Learning Based Resolution Enhancement of Iris Images34
A new algorithm for resolution enhancement of iris images captured by the low resolution camera in less cooperative situations is proposed and the prior probability relation between the information of different frequency bands of irIS features useful for recognition is firstly learned and incorporated into resolution enhancement algorithms to recover the lost information for the seriously blurred images.
- Geometric deep learning methods and applications in 3D structure-based drug design33
This review provides insights into geometric deep learning methods and advanced applications of 3D SBDD that will be of relevance for the drug discovery community.
- A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection33
This survey provides a comprehensive review of recent leading approaches in the TwGL field from three dimensions, namely, reliability, explainability, and privacy protection, and gives a general categorization for existing work and review typical work for each category.
- When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14 —( tFold for CASP14 contact prediction)33
The tFold framework seamlessly combines both homologous sequences and structural decoys under an ultra‐deep network architecture to effectively capture inter‐residue interactions.
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- Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images32
An expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of the method for triple negative breast cancer.
- Disturbance-immune weight sharing for neural architecture search31
This work proposes a new disturbance-immune update strategy for model updating that constrain the training of subsequent architectures in an orthogonal space via Orthogonal gradient descent to preserve the knowledge learned by previous architectures.
- Online Adaptive Asymmetric Active Learning With Limited Budgets31
A novel Online Adaptive Asymmetric Active learning algorithm is proposed, based on a new asymmetric strategy (merging both asymmetric losses and queries strategies), and second-order optimization, which significantly accelerates the computational speed with quite slight performance degradation.
- From circle to 3-sphere: Head pose estimation by instance parameterization31
A coarse-to-fine pose estimation framework, where the unit circle and 3-sphere are employed to model the manifold topology on the coarse and fine layer respectively, that can uniformly factorize multiple factors in an instance parametric subspace, where novel inputs can be synthesized under a generative framework.
- Multi-view matrix decomposition: a new scheme for exploring discriminative information31
This paper proposes a new multi-view, low-rank, and sparse matrix decomposition scheme to seamlessly integrate diverse yet complementary information stemming from multiple views and enjoys superior performance over the state-of-the-art approaches.
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- MARS: a motif-based autoregressive model for retrosynthesis prediction24
A novel end-to-end graph generation model for retrosynthesis prediction, which sequentially identifies the reaction center, generates the synthons, and adds motifs to the synthons to generate reactants, which achieves lower prediction complexity than adding atoms and demonstrates superior performance than adding leaving groups.
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- Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier24
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- Nonparametric Topic Modeling with Neural Inference23
This work proposes iTM-VAE, a nonparametric topic models with Auto-Encoding Variational Bayes, where the topics are treated as trainable parameters and the document-specific topic proportions are obtained by a stick-breaking construction and introduces a hyper-prior technique to model the uncertainty of the prior parameter.
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- PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning20
A novel PSSM-Distil framework for PSSP on low-quality P SSM, which not only enhances the PSSS feature at a lower level but also aligns the feature distribution at a higher level, and supports a pre-trained protein sequence language BERT model to provide auxiliary information.
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- Improving Generative Adversarial Networks With Local Coordinate Coding20
This paper proposes a GAN model with local coordinate coding (LCC), termed LCCGAN, and proposes an LCC sampling method in L CCGAN to sample meaningful points from the latent manifold, and derives the generalization bound for both LccGAN and LCCorgan++ and proves that a low-dimensional input is sufficient to achieve good generalization performance.
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- Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics19
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- Facial Image-to-Video Translation by a Hidden Affine Transformation19
This work derives an inverse formulation to the affine transformation, enabling automatic inference of the facial expression intensities from videos, and could improve users' communication experience by enabling them to conveniently and creatively produce expression GIFs, which are popular art forms in online messaging and social networks.
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- Online Compact Convexified Factorization Machine18
An Online Compact Convex Factorization Machine (OCCFM) algorithm that eschews the projection operation with efficient linear optimization steps is proposed and it is proved that the developed algorithm achieves a sub-linear regret bound.
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- TranSlider: Transfer Ensemble Learning from Exploitation to Exploration13
The concept of transfer ensemble learning is introduced, a new direction to tackle the over-fitting of transfer strategies, and the proposed Transferability Slider (TranSlider) achieves the state-of-the-art on comprehensive benchmark datasets.
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- Boost Test-Time Performance with Closed-Loop Inference8
A general Closed-Loop Inference (CLI) method that first devise a filtering criterion to identify those hard-classified test samples that need additional inference loops and construct looped inference, so that the original erroneous predictions on these hard test samples can be corrected with little additional effort.
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- Pareto-aware Neural Architecture Generation for Diverse Computational Budgets7
A Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pare to optimal architecture for any given budget via inference, which not only greatly reduces the overall search cost but also improves the search results.
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- Adaptive Proximal Average Approximation for Composite Convex Minimization6
Preliminary experimental results on overlapping group Lasso and graph-guided fused Lasso problems indicate that the proposed method is almost five times faster than the state-of-the-art PA-APG method and therefore suitable for higher-precision required optimization.
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- DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup5
A general framework to solve the above two challenges simultaneously, using distributionally robust optimization to mitigate the negative effects caused by data heterogeneity paradigm to sample clients based on a learnable distribution at each iteration is proposed.
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- Curriculum Graph Poisoning3
This paper revisits the bi-level optimization problem in graph poisoning and proposes a novel graph poisoning method, termed Curriculum Graph Poisoning (CuGPo), inspired by curriculum learning, which achieves state-of-the-art performance inGraph poisoning attacks.
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- A Unified Random Walk, Its Induced Laplacians and Spectral Convolutions for Deep Hypergraph Learning2
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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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