10 papers
Expert Pyramid Tuning: Efficient Parameter Fine-Tuning for Expertise-Driven Task Allocation
Jia-Chen Zhang, Zhen-Wei Yan, Yu-Jie Xiong +1
Parameter-Efficient Fine-Tuning (PEFT) has become a dominant paradigm for deploying LLMs in multi-task scenarios due to its extreme parameter efficiency. While Mixture-of-Experts (…
Multi-Sample Anti-Aliasing and Constrained Optimization for 3D Gaussian Splatting
Zheng Zhou, Jia-Chen Zhang, Yu-Jie Xiong +1
Recent advances in 3D Gaussian splatting have significantly improved real-time novel view synthesis, yet insufficient geometric constraints during scene optimization often result i…
Wavelet Mixture of Experts for Time Series Forecasting
Zheng Zhou, Yu-Jie Xiong, Jia-Chen Zhang +2
The field of time series forecasting is rapidly advancing, with recent large-scale Transformers and lightweight Multilayer Perceptron (MLP) models showing strong predictive perform…
Subject or Style: Adaptive and Training-Free Mixture of LoRAs
Jia-Chen Zhang, Yu-Jie Xiong
Fine-tuning models via Low-Rank Adaptation (LoRA) demonstrates remarkable performance in subject-driven or style-driven generation tasks. Studies have explored combinations of diff…
Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
Zheng Zhou, Yu-Jie Xiong, Jia-Chen Zhang +3
The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) through explicit scene representation, enabling real-time photorealistic renderi…
CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation
Jia-Chen Zhang, Zheng Zhou, Yu-Jie Xiong +2
Training data has been proven to be one of the most critical components in training generative AI. However, obtaining high-quality data remains challenging, with data privacy issue…