← Back to papers

2024 AUTOML AutoML 2024

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

Authors

Zhiqiang Tang , Haoyang Fang , Su Zhou , Taojiannan Yang , Zihan Zhong , Cuixiong Hu , Katrin Kirchhoff , George Karypis

Related papers

Improving Transfer Learning by means of Ensemble Learning and Swarm Intelligence-based Neuroevolution 2024

FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search 2024

Training and Cross-Validating Machine Learning Pipelines with Limited Memory 2024

Introducing HoNCAML: Holistic No-Code Auto Machine Learning 2024

Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework 2024