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

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

Zhiqiang Tang
Haoyang Fang
Su Zhou
Taojiannan Yang
Zihan Zhong
Tony Hu
Katrin Kirchhoff
George Karypis
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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
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Zihan Zhong
Zhiqiang Tang
Tong He
Haoyang Fang
Chun Yuan
30
40
0
31 Jan 2024
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
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
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
Learning Deep Representations of Fine-grained Visual Descriptions
Scott E. Reed
Zeynep Akata
Bernt Schiele
Honglak Lee
OCL
VLM
155
804
0
17 May 2016