7 papers
A Closer Look at Deep Learning Methods on Tabular Datasets
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai +2
Tabular data is prevalent across diverse domains in machine learning. With the rapid progress of deep tabular prediction methods, especially pretrained (foundation) models, there i…
Parrot: Multilingual Visual Instruction Tuning
Hai-Long Sun, Da-Wei Zhou, Yang Li +8
The rapid development of Multimodal Large Language Models (MLLMs), such as GPT-4o, marks a significant step toward artificial general intelligence. Existing methods typically align…
Bridge the Modality and Capability Gaps in Vision-Language Model Selection
Chao Yi, Yu-Hang He, De-Chuan Zhan +1
Vision Language Models (VLMs) excel in zero-shot image classification by pairing images with textual category names. The expanding variety of Pre-Trained VLMs enhances the likeliho…
PILOT: A Pre-Trained Model-Based Continual Learning Toolbox
Hai-Long Sun, Da-Wei Zhou, De-Chuan Zhan +1
While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing wit…
Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later
Han-Jia Ye, Huai-Hong Yin, De-Chuan Zhan +1
The widespread enthusiasm for deep learning has recently expanded into the domain of tabular data. Recognizing that the advancement in deep tabular methods is often inspired by cla…
Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective
Han-Jia Ye, Qi-Le Zhou, Huai-Hong Yin +2
Pre-training is prevalent in deep learning for vision and text data, leveraging knowledge from other datasets to enhance downstream tasks. However, for tabular data, the inherent h…