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Found 14 papers, 7 papers with code
PGformer: Proxy-Bridged Game Transformer for Multi-Person Extremely Interactive Motion Prediction
In this paper, we focus on motion prediction for multiple persons with extreme collaborations and attempt to explore the relationships between the highly interactive persons' motion trajectories.
Cross-Layer Retrospective Retrieving via Layer Attention
Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields.
ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data
Though deep neural networks have gained enormous successes in various fields (e. g., computer vision) with supervised learning, they have so far been still trailing after the performances of GBDTs on tabular data.
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction
Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction.
Robust Training of Graph Neural Networks via Noise Governance
To address these issues, we propose a novel RTGNN (Robust Training of Graph Neural Networks via Noise Governance) framework that achieves better robustness by learning to explicitly govern label noise.
ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases
Electrocardiogram (ECG) is a widely used non-invasive diagnostic tool for heart diseases.
Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network
Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation
In this paper, we propose a new method called Dilated Transformer, which conducts self-attention for pair-wise patch relations captured alternately in local and global scopes.
DANets: Deep Abstract Networks for Tabular Data Classification and Regression
A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks.
Electrocardio Panorama: Synthesizing New ECG Views with Self-supervision
For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints.
Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods
The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e. g., the Tanner-Whitehouse method) for bone age assessment.
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels
In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities.
A Hierarchical Graph Network for 3D Object Detection on Point Clouds
Next, a new GConv based Proposal Reasoning Module reasons on the proposals considering the global scene semantics, and the bounding boxes are then predicted.
Ranked #13 on
3D Object Detection
on SUN-RGBD val
