6 citations · 6 across the 8 of their papers we have counts for
8 papers
Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
TabLDM Team, Penghui Wang, Wei Liu +12
We introduce Xiaomi-TabLDM, a tabular large data foundation model for classification and regression via in-context learning, which delivers superior prediction accuracy without req…
Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance
Chunxiao Liu, Wei Liu, Anbin Xiong +1
All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption…
Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
Chunxiao Liu, Wei Liu, Anbin Xiong +1
Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances h…
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Lin Li, Jiawei Huang, Qihao Quan +7
In this paper, we propose the first VL\underline{\textbf{M}} \underline{\textbf{a}}gentic \underline{\textbf{r}}easoning framework for few-\underline{\textbf{s}}hot multimodal \und…
WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting
Shunyu Wu, Jiawei Huang, Weibin Feng +6
Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Compleme…
Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
Jiahui Zhou, Dan Li, Boxin Li +6
Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in larg…
Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model
Kai Tong, Kang Pan, Xiao Zhang +5
Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and co…
Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation
Guanhua Chen, Wenhan Yu, Xiao Lu +3
While Retrieval-Augmented Generation (RAG) plays a crucial role in the application of Large Language Models (LLMs), existing retrieval methods in knowledge-dense domains like law a…