Search Results for author:
Found 145 papers, 62 papers with code
Forest Sparsity for Multi-channel Compressive Sensing
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated.
Compressive Sensing MRI with Wavelet Tree Sparsity
On the other side, some algorithms have been proposed for tree sparsity regularization, but few of them has validated the benefit of tree structure in CS-MRI.
Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction
In this paper, we propose a novel algorithm for structured sparsity reconstruction.
Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity
In this paper, we propose a novel method for image fusion from a high resolution panchromatic image and a low resolution multispectral image at the same geographical location.
SIRF: Simultaneous Image Registration and Fusion in A Unified Framework
In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location.
Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction
The convergence rate of the proposed algorithm is almost the same as that of the traditional IRLS algorithms, that is, exponentially fast.
Deep Sparse Representation for Robust Image Registration
The definition of the similarity measure is an essential component in image registration.
Scalable Sequential Spectral Clustering
In the past decades, Spectral Clustering (SC) has become one of the most effective clustering approaches.
Track Facial Points in Unconstrained Videos
Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame.
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention
In this paper, we propose a network cohesion constrained (actor-critic) Reinforcement Learning (RL) method for mHealth.
WSISA: Making Survival Prediction From Whole Slide Histopathological Images
Different from existing state-of-the-arts image-based survival models which extract features using some patches from small regions of WSIs, the proposed framework can efficiently exploit and utilize all discriminative patterns in WSIs to predict patients' survival status.
Learning Graph While Training: An Evolving Graph Convolutional Neural Network
Convolution Neural Networks on Graphs are important generalization and extension of classical CNNs.
Group-driven Reinforcement Learning for Personalized mHealth Intervention
Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on people's health.
Robust Contextual Bandit via the Capped-$\ell_{2}$ norm
In the critic updating, the capped-$\ell_{2}$ norm is used to measure the approximation error, which prevents outliers from dominating our objective.
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatio-temporal data in various disciplines.
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.
Adaptive Graph Convolutional Neural Networks
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks.
Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions
Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.
End-to-End Learning of Motion Representation for Video Understanding
Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.
Ranked #42 on
Action Recognition
on UCF101
Adaptive Cost-sensitive Online Classification
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.
Adversarial Learning with Local Coordinate Coding
Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e. g., Gaussian noises).
Nonparametric Topic Modeling with Neural Inference
Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Large-scale distributed optimization is of great importance in various applications.
Transfer Learning via Learning to Transfer
In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.
On the Acceleration of L-BFGS with Second-Order Information and Stochastic Batches
This paper proposes a framework of L-BFGS based on the (approximate) second-order information with stochastic batches, as a novel approach to the finite-sum minimization problems.
Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays
Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice.
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip.
Weakly Supervised Region Proposal Network and Object Detection
The Convolutional Neural Network (CNN) based region proposal generation method (i. e. region proposal network), trained using bounding box annotations, is an essential component in modern fully supervised object detectors.
Adaptive Sampling Towards Fast Graph Representation Learning
Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices.
Ranked #2 on
Node Classification
on Pubmed Full-supervised
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.
Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching
We study the joint distribution matching problem which aims at learning bidirectional mappings to match the joint distribution of two domains.
Open-Ended Question Answering
Unsupervised Image-To-Image Translation
+2
Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved.
PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks
Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.
Discrimination-aware Channel Pruning for Deep Neural Networks
Channel pruning is one of the predominant approaches for deep model compression.
Progressive Feature Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain.
Ranked #7 on
Domain Adaptation
on SVHN-to-MNIST
An Efficient Approach to Informative Feature Extraction from Multimodal Data
We further generalize the framework to handle more than two modalities and missing modalities.
Weakly Supervised Dense Event Captioning in Videos
Dense event captioning aims to detect and describe all events of interest contained in a video.
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.
Ranked #10 on
Graph Classification
on D&D
Hierarchically Structured Meta-learning
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks.
RaFM: Rank-Aware Factorization Machines
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation.
Semi-supervised Learning with Contrastive Predicative Coding
Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain.
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
Unsupervised Adversarial Graph Alignment with Graph Embedding
In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.
Ranked #1 on
Node Classification
on Pubmed Full-supervised
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
To this end, we begin by investigating the theoretical connections between graph signal processing and graph embedding models in a principled way and formulate the graph embedding model as a general graph signal process with corresponding graph filter.
Graph Convolutional Networks for Temporal Action Localization
Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
Ranked #4 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
Transferable Neural Processes for Hyperparameter Optimization
Automated machine learning aims to automate the whole process of machine learning, including model configuration.
Octave Graph Convolutional Network
Many variants of Graph Convolutional Networks (GCNs) for representation learning have been proposed recently and have achieved fruitful results in various domains.
Molecular Graph Enhanced Transformer for Retrosynthesis Prediction
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers.
Ranked #17 on
Single-step retrosynthesis
on USPTO-50k
Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation
We investigate the posterior collapse problem of current RNN-based VAEs for molecule sequence generation.
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there have been extensive studies.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations.
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).
Online Adaptive Asymmetric Active Learning with Limited Budgets
In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.
DTWNet: a Dynamic Time Warping Network
Dynamic Time Warping (DTW) is widely used as a similarity measure in various domains.
Discrimination-aware Network Pruning for Deep Model Compression
In this paper, we propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power.
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.
Graph Ordering: Towards the Optimal by Learning
However, regardless of the fruitful progress, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks.
Adversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.
Adversarial Domain Adaptation for Cell Segmentation
In this paper, we propose a network named CellSegUDA for cell segmentation on the unlabeled dataset (target domain).
Graph Representation Learning via Graphical Mutual Information Maximization
The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision.
Joint Wasserstein Distribution Matching
However, the resultant optimization problem is still intractable.
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.
Context-Aware Domain Adaptation in Semantic Segmentation
To achieve this goal, we design two cross-domain attention modules to adapt context dependencies from both spatial and channel views.
Ranked #27 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation.
Ranked #26 on
Domain Adaptation
on SYNTHIA-to-Cityscapes
Spectral Graph Attention Network with Fast Eigen-approximation
In this paper, we first introduce the attention mechanism in the spectral domain of graphs and present Spectral Graph Attention Network (SpGAT) that learns representations for different frequency components regarding weighted filters and graph wavelets bases.
Disturbance-immune Weight Sharing for Neural Architecture Search
To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation
To satisfy the stringent requirements on computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks.
Neural Architecture Search
Real-Time Semantic Segmentation
+1
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
Towards Fast Adaptation of Neural Architectures with Meta Learning
Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks.
Multi-View Graph Neural Networks for Molecular Property Prediction
Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties.
Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments.
Self-Supervised Graph Transformer on Large-Scale Molecular Data
We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
Ranked #4 on
Molecular Property Prediction
on QM7
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical medicine.
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods.
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.
Improving Generalization in Meta-learning via Task Augmentation
Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.
Improving Generative Adversarial Networks with Local Coordinate Coding
In this paper, rather than sampling from the predefined prior distribution, we propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
Tackling Over-Smoothing for General Graph Convolutional Networks
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification.
User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models.
Microscope Based HER2 Scoring System
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks
We evaluated our methods on two large cancer whole slide images datasets and our results suggest that the proposed approach is more effective and suitable for large datasets and has better interpretability in locating important patterns and features that contribute to accurate cancer survival predictions.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
Graph Information Bottleneck for Subgraph Recognition
In this paper, we propose a framework of Graph Information Bottleneck (GIB) for the subgraph recognition problem in deep graph learning.
On Self-Distilling Graph Neural Network
Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.
RetroXpert: Decompose Retrosynthesis Prediction like a Chemist
Retrosynthesis is the process of recursively decomposing target molecules into available building blocks.
Deep Multimodal Fusion by Channel Exchanging
Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.
Adversarial Sparse Transformer for Time Series Forecasting
Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Nevertheless, it is unclear how parameter sharing affects the searching process.
Hierarchical Graph Capsule Network
Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.
Pareto-Frontier-aware Neural Architecture Search
To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary.
Learning Diverse-Structured Networks for Adversarial Robustness
In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones in standard training (ST).
Towards Accurate and Compact Architectures via Neural Architecture Transformer
To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning
Meta-learning for offline reinforcement learning (OMRL) is an understudied problem with tremendous potential impact by enabling RL algorithms in many real-world applications.
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.
Diversified Multiscale Graph Learning with Graph Self-Correction
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.
Recognizing Predictive Substructures with Subgraph Information Bottleneck
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning.
Deep Implicit Statistical Shape Models for 3D Medical Image Delineation
DISSMs use a deep implicit surface representation to produce a compact and descriptive shape latent space that permits statistical models of anatomical variance.
Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed
In this paper, we propose Graph Neural Network models for both CS and ACS problems, i. e., Query Driven-GNN and Attributed Query Driven-GNN.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modeling Structure Refinement
Protein structure prediction has been a grand challenge for over 50 years, owing to its broad scientific and application interests.
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation
Recent studies imply that deep neural networks are vulnerable to adversarial examples -- inputs with a slight but intentional perturbation are incorrectly classified by the network.
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.
Learning Graphon Autoencoders for Generative Graph Modeling
A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions
This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs, which relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels.
Frustratingly Easy Transferability Estimation
Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.
TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation
With the recent exponential increase in applying Vision Transformer (ViT) to vision tasks, the capability of ViT in adapting cross-domain knowledge, however, remains unexplored in the literature.
Local Augmentation for Graph Neural Networks
To address this, we propose a simple and efficient data augmentation strategy, local augmentation, to learn the distribution of the node features of the neighbors conditioned on the central node's feature and enhance GNN's expressive power with generated features.
Constrained Graph Mechanics Networks
In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.
Weakly Supervised Graph Clustering
Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels
Semi-supervised node classification on graphs is a fundamental problem in graph mining that uses a small set of labeled nodes and many unlabeled nodes for training, so that its performance is quite sensitive to the quality of the node labels.
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable.
Ranked #5 on
Image Classification
on mini WebVision 1.0
Not All Low-Pass Filters are Robust in Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data.
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
More recently, there has been a surge of interest in employing machine learning approaches to expedite the drug discovery process where virtual screening for hit discovery and ADMET prediction for lead optimization play essential roles.
Graph Convolutional Module for Temporal Action Localization in Videos
To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms.
Ranked #2 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates.
Ranked #9 on
Single-step retrosynthesis
on USPTO-50k
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer
Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
Transformer for Graphs: An Overview from Architecture Perspective
In this survey, we provide a comprehensive review of various Graph Transformer models from the architectural design perspective.
Vision-Language Pre-Training with Triple Contrastive Learning
Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning.
Ranked #3 on
Zero-Shot Cross-Modal Retrieval
on COCO 2014
Equivariant Graph Mechanics Networks with Constraints
The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.
Boost Test-Time Performance with Closed-Loop Inference
Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0. 732 (0. 683-0. 761), and better performance when compared to other methods.
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
In this paper, we propose a general framework to solve the above two challenges simultaneously.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
Seeking Common Ground While Reserving Differences: Multiple Anatomy Collaborative Framework for Undersampled MRI Reconstruction
Four different implementations of anatomy-specific learners are presented and explored on the top of our framework in two MRI reconstruction networks.
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
To remedy this issue, we propose to select positive graph instances directly from existing graphs in the training set, which ultimately maintains the legality and similarity to the target graphs.
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
We first benchmark representative SSL methods for dense prediction tasks in pathology images.
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.
MARS: A Motif-based Autoregressive Model for Retrosynthesis Prediction
In this paper, we propose 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.
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
More critically, these independent search processes cannot share their learned knowledge (i. e., the distribution of good architectures) with each other and thus often result in limited search results.
Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical.
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge.
MIVC: Multiple Instance Visual Component for Visual-Language Models
Vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance.
Segment Any Cell: A SAM-based Auto-prompting Fine-tuning Framework for Nuclei Segmentation
To address this, we introduce Segment Any Cell (SAC), an innovative framework that enhances SAM specifically for nuclei segmentation.
Distribution-Free Fair Federated Learning with Small Samples
As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic groups becomes critically important.
PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning
In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions.
