6 papers
MDN: Parallelizing Stepwise Momentum for Delta Linear Attention
Yulong Huang, Xiang Liu, Hongxiang Huang +5
Linear Attention (LA) offers a promising paradigm for scaling large language models (LLMs) to long sequences by avoiding the quadratic complexity of self-attention. Recent LA model…
Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression
Xiang Liu, Yimin Zhou, Jinxiang Wang +9
The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms…
LyTimeT: Towards Robust and Interpretable State-Variable Discovery
Kuai Yu, Crystal Su, Xiang Liu +3
Extracting the true dynamical variables of a system from high-dimensional video is challenging due to distracting visual factors such as background motion, occlusions, and texture…
Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators
Wenhan Gao, Jian Luo, Fang Wan +4
Recently, neural operators have emerged as powerful tools for learning mappings between function spaces, enabling data-driven simulations of complex dynamics. Despite their success…
Robust Multi-generation Learned Compression of Point Cloud Attribute
Xiangzuo Liu, Zhikai Liu, PengPeng Yu +2
Existing learned point cloud attribute compression methods primarily focus on single-pass rate-distortion optimization, while overlooking the issue of cumulative distortion in mult…
WATER-GS: Toward Copyright Protection for 3D Gaussian Splatting via Universal Watermarking
Yuqi Tan, Xiang Liu, Shuzhao Xie +3
3D Gaussian Splatting (3DGS) has emerged as a pivotal technique for 3D scene representation, providing rapid rendering speeds and high fidelity. As 3DGS gains prominence, safeguard…