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Found 17 papers, 3 papers with code
Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers
To tackle these challenges, we propose BotDGT, a novel framework that not only considers the topological structure, but also effectively incorporates dynamic nature of social network.
DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling
Text-to-3D scene generation holds immense potential for the gaming, film, and architecture sectors.
DeCoF: Generated Video Detection via Frame Consistency
The escalating quality of video generated by advanced video generation methods leads to new security challenges in society, which makes generated video detection an urgent research priority.
Noise-NeRF: Hide Information in Neural Radiance Fields using Trainable Noise
Neural radiance fields (NeRF) have been proposed as an innovative 3D representation method.
2D-Guided 3D Gaussian Segmentation
Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration.
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion
As such, the aggregated language model can leverage complementary knowledge from multilingual KGs without demanding raw user data sharing.
Take History as a Mirror in Heterogeneous Federated Learning
Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data.
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears.
Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction
Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance.
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Video prediction is a complex time-series forecasting task with great potential in many use cases.
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model.
Mitigating Backdoors in Federated Learning with FLD
Federated learning allows clients to collaboratively train a global model without uploading raw data for privacy preservation.
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.
Ranked #1 on
Relation Extraction
on WebNLG
Celeritas: Fast Optimizer for Large Dataflow Graphs
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device.
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences.
Causal Incremental Graph Convolution for Recommender System Retraining
Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.
