1 citations · 1 across the 7 of their papers we have counts for
7 papers
Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
Xingyu Zheng, Xianglong Liu, Yifu Ding +4
Hardware-agnostic strategies for accelerating text-to-image diffusion, such as timestep distillation and feature caching, can reduce inference time without custom kernels or system…
Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation
Jianing Guo, Fangzheng Chen, Zihao Mao +12
Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside sim…
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Yifu Ding, Jiacheng Wang, Ge Yang +4
Mixture-of-Experts (MoE) models scale compute efficiently, yet remain expensive to deploy due to their substantial memory footprint and inference overhead. Prior compression method…
LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Jian Yang, Shawn Guo, Wei Zhang +16
Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop T…
An Empirical Study of openPangu Quantization on Ascend NPUs
Tong Shi, Jiacheng Wang, Hui Xie +4
openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs ha…
BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models
Xingyu Zheng, Xianglong Liu, Haotong Qin +7
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and effici…
DB-LLM: Accurate Dual-Binarization for Efficient LLMs
Hong Chen, Chengtao Lv, Liang Ding +8
Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical dep…