Ludwig Schmidt
Publications by Year
Research Areas
Multimodal Machine Learning Applications, Natural Language Processing Techniques, Generative Adversarial Networks and Image Synthesis, Domain Adaptation and Few-Shot Learning, Adversarial Robustness in Machine Learning
Most-Cited Works
- → LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models(2022)14 cited
- → GenEval: An object-focused framework for evaluating text-to-image alignment(2023)4 cited
- → Are aligned neural networks adversarially aligned?(2023)4 cited
- → Does progress on ImageNet transfer to real-world datasets?(2023)2 cited
- → On the Connection between Pre-training Data Diversity and Fine-tuning Robustness(2023)1 cited
- → DataComp: In search of the next generation of multimodal datasets(2023)1 cited
- → Stable and low-precision training for large-scale vision-language models(2023)
- → Quality Not Quantity: On the Interaction Between Dataset Design and Robustness of CLIP(2022)
- → Patching Open-Vocabulary Models by Interpolating Weights(2022)
- → Characterizing the Impacts of Semi-supervised Learning for Weak Supervision(2023)