AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with
Foundation Models

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

24 April 2024

Taojiannan Yang

Katrin Kirchhoff

George Karypis

Papers citing "AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models"

4 / 4 papers shown

Title
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model Zihan Zhong Zhiqiang Tang Tong He Haoyang Fang Chun Yuan 33 40 0 31 Jan 2024
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 77 268 0 13 Jul 2021
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 81 576 0 13 Mar 2020
Learning Deep Representations of Fine-grained Visual Descriptions Scott E. Reed Zeynep Akata Bernt Schiele Honglak Lee OCL VLM 160 804 0 17 May 2016