Search Results for author:
Found 15 papers, 1 papers with code
THeGCN: Temporal Heterophilic Graph Convolutional Network
Furthermore, we highlight that the edge heterophily issue and the temporal heterophily issue often co-exist in event-based continuous graphs, giving rise to the temporal edge heterophily challenge.
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data.
Discrete-state Continuous-time Diffusion for Graph Generation
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design.
TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction
Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking.
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side.
Invariant Graph Transformer
Based on the self-attention module, our proposed invariant graph Transformer (IGT) can achieve fine-grained, more specifically, node-level and virtual node-level intervention.
Tackling Diverse Minorities in Imbalanced Classification
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Rectify ViT Shortcut Learning by Visual Saliency
In this work, we propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data.
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances.
