39 citations · 239 across the 55 of their papers we have counts for
57 papers
Improving Test-Time Scaling with Adaptive Looped Transformers
Yichen You, Tianyu Fu, Aosong Feng +4
Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-looped models at matched paramet…
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Enshu Liu, Xuefei Ning, Yu Wang +1
Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in…
CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation
Kaiyi Huang, Yukun Huang, Yu Li +8
Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical se…
SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer
Tongcheng Fang, Hanling Zhang, Ruiqi Xie +8
Diffusion Transformers have demonstrated remarkable performance in video generation. However, their long input sequences incur substantial latency due to the quadratic complexity o…
SJD++: Improved Speculative Jacobi Decoding for Training-free Acceleration of Discrete Auto-regressive Text-to-Image Generation
Yao Teng, Zhihuan Jiang, Han Shi +6
Large autoregressive models can generate high-quality, high-resolution images but suffer from slow generation speed, because these models require hundreds to thousands of sequentia…
Distilled Decoding 2: One-step Sampling of Image Auto-regressive Models with Conditional Score Distillation
Enshu Liu, Qian Chen, Xuefei Ning +4
Image Auto-regressive (AR) models have emerged as a powerful paradigm of visual generative models. Despite their promising performance, they suffer from slow generation speed due t…
Understanding the Ability of LLMs to Handle Character-Level Perturbation
Anyuan Zhuo, Xuefei Ning, Ningyuan Li +3
This work investigates the resilience of contemporary large language models (LLMs) against frequent character-level perturbations. We examine three types of character-level perturb…
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Zinan Lin, Enshu Liu, Xuefei Ning +3
Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoi…
FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
Kaiyi Huang, Yukun Huang, Xintao Wang +6
AI-driven content creation has shown potential in film production. However, existing film generation systems struggle to implement cinematic principles and thus fail to generate pr…
AgentSociety Challenge: Designing LLM Agents for User Modeling and Recommendation on Web Platforms
Yuwei Yan, Yu Shang, Qingbin Zeng +9
The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enha…
FrameFusion: Combining Similarity and Importance for Video Token Reduction on Large Vision Language Models
Tianyu Fu, Tengxuan Liu, Qinghao Han +5
The increasing demand to process long and high-resolution videos significantly burdens Large Vision-Language Models (LVLMs) due to the enormous number of visual tokens. Existing to…
GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration
Kaiyi Huang, Yukun Huang, Xuefei Ning +3
Text-to-video generation models have shown significant progress in the recent years. However, they still struggle with generating complex dynamic scenes based on compositional text…
MBQ: Modality-Balanced Quantization for Large Vision-Language Models
Shiyao Li, Yingchun Hu, Xuefei Ning +10
Vision-Language Models (VLMs) have enabled a variety of real-world applications. The large parameter size of VLMs brings large memory and computation overhead which poses significa…
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
Enshu Liu, Xuefei Ning, Yu Wang +1
Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process. We ask an ambi…
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization
Rui Xie, Tianchen Zhao, Zhihang Yuan +5
Visual Autoregressive (VAR) has emerged as a promising approach in image generation, offering competitive potential and performance comparable to diffusion-based models. However, c…
Towards Accurate and Efficient Sub-8-Bit Integer Training
Wenjin Guo, Donglai Liu, Weiying Xie +7
Neural network training is a memory- and compute-intensive task. Quantization, which enables low-bitwidth formats in training, can significantly mitigate the workload. To reduce qu…
CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios
Luning Wang, Shiyao Li, Xuefei Ning +4
Large Language Models (LLMs) have been widely adopted to process long-context tasks. However, the large memory overhead of the key-value (KV) cache poses significant challenges in…
Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding
Yao Teng, Han Shi, Xian Liu +5
The current large auto-regressive models can generate high-quality, high-resolution images, but these models require hundreds or even thousands of steps of next-token prediction du…
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
Enshu Liu, Junyi Zhu, Zinan Lin +6
The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their subst…
Mixture of Attention Spans: Optimizing LLM Inference Efficiency with Heterogeneous Sliding-Window Lengths
Tianyu Fu, Haofeng Huang, Xuefei Ning +10
Sliding-window attention offers a hardware-efficient solution to the memory and throughput challenges of Large Language Models (LLMs) in long-context scenarios. Existing methods ty…
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Xuefei Ning, Zifu Wang, Shiyao Li +7
Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also t…
DiTFastAttn: Attention Compression for Diffusion Transformer Models
Zhihang Yuan, Hanling Zhang, Pu Lu +6
Diffusion Transformers (DiT) excel at image and video generation but face computational challenges due to the quadratic complexity of self-attention operators. We propose DiTFastAt…
ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
Tianchen Zhao, Tongcheng Fang, Haofeng Huang +10
Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, la…
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
Tianchen Zhao, Xuefei Ning, Tongcheng Fang +6
Diffusion models have achieved significant visual generation quality. However, their significant computational and memory costs pose challenge for their application on resource-con…