10 papers
Cubit: Token Mixer with Kernel Ridge Regression
Chuanyang Zheng, Jiankai Sun, Yihang Gao +6
Since its introduction in 2017, the Transformer has become one of the most widely adopted architectures in modern deep learning. Despite extensive efforts to improve positional enc…
Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
Zhenggang Tang, Yuehao Wang, Yuchen Fan +9
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate…
GeoNorm: Unify Pre-Norm and Post-Norm with Geodesic Optimization
Chuanyang Zheng, Jiankai Sun, Yihang Gao +11
The placement of normalization layers, specifically Pre-Norm and Post-Norm, remains an open question in Transformer architecture design. In this work, we rethink these approaches t…
Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
Chuanyang Zheng, Jiankai Sun, Yihang Gao +13
Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on as the router score funct…
SAS: Simulated Attention Score
Chuanyang Zheng, Jiankai Sun, Yihang Gao +12
The attention mechanism is a core component of the Transformer architecture. Various methods have been developed to compute attention scores, including multi-head attention (MHA),…
FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting
Hengyu Liu, Yuehao Wang, Chenxin Li +6
3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands…
Steepest Descent Density Control for Compact 3D Gaussian Splatting
Peihao Wang, Yuehao Wang, Dilin Wang +8
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time, high-resolution novel view synthesis. By representing scenes as a mixture of Gaussian primitives, 3D…
Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing
Peihao Wang, Ruisi Cai, Yuehao Wang +4
Structured State Space Models (SSMs) have emerged as alternatives to transformers. While SSMs are often regarded as effective in capturing long-sequence dependencies, we rigorously…
Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method
Yan Zheng, Zhenxiao Liang, Xiaoyan Cong +4
We explore the oscillatory behavior observed in inversion methods applied to large-scale text-to-image diffusion models, with a focus on the "Flux" model. By employing a fixed-poin…
Bilateral Guided Radiance Field Processing
Yuehao Wang, Chaoyi Wang, Bingchen Gong +1
Neural Radiance Fields (NeRF) achieves unprecedented performance in synthesizing novel view synthesis, utilizing multi-view consistency. When capturing multiple inputs, image signa…