143 citations · 500 across the 20 of their papers we have counts for
5 papers · 1 filter
SALS: Sparse Attention in Latent Space for KV cache Compression
Junlin Mu, Hantao Huang, Jihang Zhang +3
Large Language Models capable of handling extended contexts are in high demand, yet their inference remains challenging due to substantial Key-Value cache size and high memory band…
GrassNet: State Space Model Meets Graph Neural Network
Gongpei Zhao, Tao Wang, Yi Jin +3
Designing spectral convolutional networks is a formidable task in graph learning. In traditional spectral graph neural networks (GNNs), polynomial-based methods are commonly used t…
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Gongpei Zhao, Tao Wang, Congyan Lang +3
Graph neural networks are recognized for their strong performance across various applications, with the backpropagation algorithm playing a central role in the development of most…
GLAN: A Graph-based Linear Assignment Network
He Liu, Tao Wang, Congyan Lang +3
Differentiable solvers for the linear assignment problem (LAP) have attracted much research attention in recent years, which are usually embedded into learning frameworks as compon…
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Gengyu Lyu, Songhe Feng, Yi Jin +3
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing m…