6 papers · 1 filter
Semantic DLM+: Improving Diffusion Language Models through Bias-variance Trade-off in Transition Kernel Design
Keyue Jiang, Yuxiang Wang, Yanan Zhao +7
Diffusion Language Models (DLMs) have demonstrated strong scaling capacity as alternatives to autoregressive language models. However, their performance is highly sensitive to the…
On the Trainability of Masked Diffusion Language Models via Blockwise Locality
Yuxiang Wang, Yu Xiang, Baojian Zhou +4
Masked diffusion language models (MDMs) have recently emerged as a promising alternative to standard autoregressive large language models (AR-LLMs), yet their optimization can be s…
Accelerated Evolving Set Processes for Local PageRank Computation
Binbin Huang, Luo Luo, Yanghua Xiao +2
This work proposes a novel framework based on nested evolving set processes to accelerate Personalized PageRank (PPR) computation. At each stage of the process, we employ a localiz…
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
Xingzhi Guo, Silong Wang, Baojian Zhou +2
Real-world graphs grow rapidly with edge and vertex insertions over time, motivating the problem of efficiently maintaining robust node representation over evolving graphs. Recent…
Iterative Methods via Locally Evolving Set Process
Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh +3
Given the damping factor and precision tolerance , \citet{andersen2006local} introduced Approximate Personalized PageRank (APPR), the \textit{de facto local method} for appr…
Faster Local Solvers for Graph Diffusion Equations
Jiahe Bai, Baojian Zhou, Deqing Yang +1
Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks,…