2 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
From Betti Numbers to Persistence Diagrams: A Hybrid Quantum Algorithm for Topological Data Analysis
Dong Liu
Persistence diagrams serve as a core tool in topological data analysis, playing a crucial role in pathological monitoring, drug discovery, and materials design. However, existing q…
Fiber Bundle Networks: A Geometric Machine Learning Paradigm
Dong Liu
We propose Fiber Bundle Networks (FiberNet), a novel machine learning framework integrating differential geometry with machine learning. Unlike traditional deep neural networks rel…
Re-Key-Free, Risky-Free: Adaptable Model Usage Control
Zihan Wang, Zhongkui Ma, Xinguo Feng +6
Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in…
TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
Dong Liu, Yanxuan Yu
Serving large language models (LLMs) efficiently remains challenging due to the high memory and latency overhead of key-value (KV) cache access during autoregressive decoding. We p…
Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
Like Jian, Dong Liu
Federated learning (FL) enables decentralized clients to train a model collaboratively without sharing local data. A key distinction between FL and centralized learning is that cli…
QuickMerge++: Fast Token Merging with Autoregressive Prior
Dong Liu, Yanxuan Yu
As generative models scale to larger inputs across language, vision, and video domains, the cost of token-level computation has become a key bottleneck. While prior work suggests t…
PiKV: KV Cache Management System for Mixture of Experts
Dong Liu, Yanxuan Yu, Ben Lengerich +1
As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache storage has become a major bottleneck…
FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation
Dong Liu, Yanxuan Yu, Jiayi Zhang +3
Diffusion Transformers (DiT) are powerful generative models but remain computationally intensive due to their iterative structure and deep transformer stacks. To alleviate this ine…