Search Results for author:
Found 90 papers, 43 papers with code
Large Language-Geometry Model: When LLM meets Equivariance
Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications.
Diffusion Models for Molecules: A Survey of Methods and Tasks
This survey aims to facilitate understanding and further flourishing development in this area.
Natural Language-Assisted Multi-modal Medication Recommendation
Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities.
ACQ: A Unified Framework for Automated Programmatic Creativity in Online Advertising
ACQ comprises two components: a prediction module to estimate the cost of a photo under different numbers of ad creatives, and an allocation module to decide the quota for photos considering their estimated costs in the prediction module.
Graph Pre-Training Models Are Strong Anomaly Detectors
Graph Anomaly Detection (GAD) is a challenging and practical research topic where Graph Neural Networks (GNNs) have recently shown promising results.
Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
Moreover, our CoI agent is budget-friendly, with a minimum cost of \$0. 50 to generate a candidate idea and its corresponding experimental design.
Adaptive Coordinators and Prompts on Heterogeneous Graphs for Cross-Domain Recommendations
However, integrating multi-domain knowledge for the cross-domain recommendation is very hard due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain's performance.
GenCA: A Text-conditioned Generative Model for Realistic and Drivable Codec Avatars
Specifically, we integrate the generative and editing capabilities of latent diffusion models with a strong prior model for avatar expression driving.
Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning
Moreover, we show that DEGNN is data efficient, learning with less data, and can generalize across scenarios such as unobserved orientation.
MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series
Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels.
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task.
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
We design a Hierarchical Adaptive Alignment model to concurrently learn the fine-grained fragment correspondence between two modalities and align these representations of fragments in three levels.
Annotation-guided Protein Design with Multi-Level Domain Alignment
In this paper, we propose Protein-Annotation Alignment Generation, PAAG, a multi-modality protein design framework that integrates the textual annotations extracted from protein database for controllable generation in sequence space.
All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular.
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data.
Deep Insights into Noisy Pseudo Labeling on Graph Data
We first present the error analysis of PL strategy by showing that the error is bounded by the confidence of PL threshold and consistency of multi-view prediction.
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.
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.
Vision Guided MIMO Radar Beamforming for Enhanced Vital Signs Detection in Crowds
Radar as a remote sensing technology has been used to analyze human activity for decades.
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.
Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax
To address these challenges, we formulate the micro perspective mobility modeling into computing the relevance score between a diffusion and a location, conditional on a geometric graph.
Vertical Federated Linear Contextual Bandits
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i. e., contextual information is vertically distributed over different departments.
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
Pretraining molecular representation models without labels is fundamental to various applications.
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.
Semi-Supervised Hierarchical Graph Classification
We study the node classification problem in the hierarchical graph where a 'node' is a graph instance.
Towards Diverse and Natural Scene-aware 3D Human Motion Synthesis
The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications.
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.
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.
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones.
Ranked #1 on
Graph Classification
on HIV
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.
Ranked #7 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
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.
Equivariant Graph Hierarchy-Based Neural Networks
Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.
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.
Geometrically Equivariant Graph Neural Networks: A Survey
Many scientific problems require to process data in the form of geometric graphs.
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.
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.
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.
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)
Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements
In this paper, we make the first attempt to reconstruct 3D interacting hands from monocular single RGB images.
VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds
3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding.
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.
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.
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.
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body.
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.
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.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
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.
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.
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.
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.
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.
Recognizing Predictive Substructures with Subgraph Information Bottleneck
The emergence of Graph Convolutional Network (GCN) has greatly boosted the progress of graph learning.
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.
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).
Chasing the Tail in Monocular 3D Human Reconstruction with Prototype Memory
The reason is that most of the current models perform regression based on a single human prototype, which is similar to common poses while far from the rare poses.
Hierarchical Graph Capsule Network
Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data.
Non-Contact Vital Signs Detection with UAV-Borne Radars
Aggregating radar measurements with the information from other sensors is broadening the applications of drones in life-critical situations.
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.
On Self-Distilling Graph Neural Network
Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.
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.
Dirichlet Graph Variational Autoencoder
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
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.
FrankMocap: Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration
To construct FrankMocap, we build the state-of-the-art monocular 3D "hand" motion capture method by taking the hand part of the whole body parametric model (SMPL-X).
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.
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
Towards Feature-free TSP Solver Selection: A Deep Learning Approach
To solve TSP efficiently, in addition to developing new TSP solvers, it needs to find a per-instance solver for each TSP instance, which is known as the TSP solver selection problem.
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.
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.
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.
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.
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.
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.
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 #10 on
Single-step retrosynthesis
on USPTO-50k
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)
Delving Deep Into Hybrid Annotations for 3D Human Recovery in the Wild
Specifically, we focus on the challenging task of in-the-wild 3D human recovery from single images when paired 3D annotations are not fully available.
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.
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
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).
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.
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 #11 on
Graph Classification
on D&D
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 #8 on
Domain Adaptation
on SVHN-to-MNIST
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
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.
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces.
Ranked #1 on
Face Identification
on IJB-A
