4 citations · 4 across the 4 of their papers we have counts for
6 papers
GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering
Zihua Yang, Zhencheng Xie, Junyang Chen +4
Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols. Tradi…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
TimeSense:Making Large Language Models Proficient in Time-Series Analysis
Zhirui Zhang, Changhua Pei, Tianyi Gao +7
In the time-series domain, an increasing number of works combine text with temporal data to leverage the reasoning capabilities of large language models (LLMs) for various downstre…
From Time Series Analysis to Question Answering: A Survey in the LLM Era
Wei Li, Zhe Xie, Yuxuan Liang +4
Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities to support tasks such as forecasting…
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
Zhe Xie, Zeyan Li, Xiao He +6
Understanding time series is crucial for its application in real-world scenarios. Recently, large language models (LLMs) have been increasingly applied to time series tasks, levera…
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
Zhe Xie, Chengxuan Liu, Yichi Zhang +3
Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism…