7 citations · 10 across the 7 of their papers we have counts for
5 papers · 1 filter
KV Cache Compression for Inference Efficiency in LLMs: A Review
Yanyu Liu, Jingying Fu, Sixiang Liu +4
Withtherapid advancement of large language models (LLMs), the context length for inference has been continuously increasing, leading to an exponential growth in the demand for Key-…
Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard
Si-Yang Liu, Qile Zhou, Han-Jia Ye
Tabular data, a fundamental data format in machine learning, is predominantly utilized in competitions and real-world applications. The performance of tabular models--such as gradi…
Representation Learning for Tabular Data: A Comprehensive Survey
Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai +2
Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabula…
TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems
Si-Yang Liu, Han-Jia Ye
TabPFN has emerged as a promising in-context learning model for tabular data, capable of directly predicting the labels of test samples given labeled training examples. It has demo…
A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
Han-Jia Ye, Si-Yang Liu, Wei-Lun Chao
Tabular datasets are inherently heterogeneous, presenting significant challenges for developing pre-trained foundation models. The recently introduced transformer-based Tabular Pri…