74 citations · 203 across the 26 of their papers we have counts for
11 papers · 1 filter
FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space
Jiahong Liu, Ram Samarth B B, Xinyu Fu +4
Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients…
Hyperbolic Multimodal Continual Learning
Jiahong Liu, Ming Shen, Xiaohao Liu +4
Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic structure across modalities. Des…
Probability-Entropy Calibration: An Elastic Indicator for Adaptive Fine-tuning
Wenhao Yu, Shaohang Wei, Jiahong Liu +5
Token-level reweighting is a simple yet effective mechanism for controlling supervised fine-tuning, but common indicators are largely one-dimensional: the ground-truth probability…
Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation
Meng Qin, Jiahong Liu, Irwin King
Graph neural networks (GNNs), which capture graph structures via a feature aggregation mechanism following the graph embedding framework, have demonstrated a powerful ability to su…
Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries
Neil He, Jiahong Liu, Buze Zhang +6
In the era of foundation models and Large Language Models (LLMs), Euclidean space has been the de facto geometric setting for machine learning architectures. However, recent litera…
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
Menglin Yang, Jialin Chen, Jinkai Tao +9
The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advan…
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
Yali Fu, Jindong Li, Jiahong Liu +3
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mai…
Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space
Menglin Yang, Harshit Verma, Delvin Ce Zhang +3
Hyperbolic geometry have shown significant potential in modeling complex structured data, particularly those with underlying tree-like and hierarchical structures. Despite the impr…
Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning
Yifei Zhang, Hao Zhu, Menglin Yang +4
Learning generalizable self-supervised graph representations for downstream tasks is challenging. To this end, Contrastive Learning (CL) has emerged as a leading approach. The embe…
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Menglin Yang, Min Zhou, Tong Zhang +5
Graph representation learning in Euclidean space, despite its widespread adoption and proven utility in many domains, often struggles to effectively capture the inherent hierarchic…
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Jiahong Liu, Menglin Yang, Min Zhou +2
Recently, hyperbolic space has risen as a promising alternative for semi-supervised graph representation learning. Many efforts have been made to design hyperbolic versions of neur…