2 citations · 3 across the 8 of their papers we have counts for
9 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…
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…
Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace
Jia-Chen Zhang, Yu-Jie Xiong, Chun-Ming Xia +2
Large language model (LLM) is considered a milestone towards achieving Artificial General Intelligence (AGI). With its advanced emergent capabilities, it adapt to a wide range of s…
Mixture of Routers
Jia-Chen Zhang, Yu-Jie Xiong, Xi-He Qiu +3
Supervised fine-tuning (SFT) is a milestone in aligning large language models with human instructions and adapting them to downstream tasks. In particular, Low-Rank Adaptation (LoR…
Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
Dong-Hai Zhu, Yu-Jie Xiong, Jia-Chen Zhang +2
Chain-of-Thought (CoT) Prompting is a dominant paradigm in Large Language Models (LLMs) to enhance complex reasoning. It guides LLMs to present multi-step reasoning, rather than ge…
LoRA : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models
Jia-Chen Zhang, Yu-Jie Xiong, He-Xi Qiu +2
Fine-tuning large language models (LLMs) with high parameter efficiency for downstream tasks has become a new paradigm. Low-Rank Adaptation (LoRA) significantly reduces the number…