85 citations · 633 across the 51 of their papers we have counts for
5 papers · 1 filter
DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models
Cheng Yin, Yankai Lin, Wang Xu +4
Does Chain-of-Thought (CoT) reasoning genuinely improve Vision Language Action (VLA) models, or does it merely add overhead? Existing CoT-VLA systems report limited and inconsisten…
ReLU Wins: Discovering Efficient Activation Functions for Sparse LLMs
Zhengyan Zhang, Yixin Song, Guanghui Yu +7
Sparse computation offers a compelling solution for the inference of Large Language Models (LLMs) in low-resource scenarios by dynamically skipping the computation of inactive neur…
Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou, Yankai Lin, Shaobo Liu +3
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However,…
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
Deli Chen, Xiaoqian Liu, Yankai Lin +4
Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. Howev…
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Deli Chen, Yankai Lin, Wei Li +3
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks. Despite their success, one severe limitation of GNNs is the over-smoothing is…