Schmidt, Ludwig

Towards Deep Learning Models Resistant to Adversarial Attacks

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 6,602

LAION-5B: An open large-scale dataset for training next generation image-text models

Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 1,022

Do ImageNet Classifiers Generalize to ImageNet?

Recht, B., Roelofs, R., Schmidt, L., & Shankar, V. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 790

Objaverse: A Universe of Annotated 3D Objects

Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsanit, K., Kembhavi, A., & Farhadi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisAdvanced Neural Network Applications

Cited by 725

Reproducible Scaling Laws for Contrastive Language-Image Learning

Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., & Jitsev, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 673

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 428

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Merrill, M. A., Shaw, A. G., Carlini, N., Li, B., Raj, H., Bercovich, I., Shi, L., Shin, J. Y., Walshe, T., Buchanan, E. K., et al. (2026) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 392

LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models

Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · Advances in Neural Information Processing Systems 35

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 325

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 307

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 303

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 294

Measuring Robustness to Natural Distribution Shifts in Image Classification

Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., & Schmidt, L. (2020) · arXiv

Adversarial Robustness in Machine LearningDomain Adaptation and Few-Shot Learning

Cited by 206

DataComp: In search of the next generation of multimodal datasets

Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 120

The Effect of Natural Distribution Shift on Question Answering Models

Miller, J., Krauth, K., Recht, B., & Schmidt, L. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 74

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 74

Editing Models with Task Arithmetic

Ilharco, G., Ribeiro, M. T., Wortsman, M., Gururangan, S., Schmidt, L., Hajishirzi, H., & Farhadi, A. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 66

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Bitton-Guetta, N., Bitton, Y., Hessel, J., Schmidt, L., Elovici, Y., Stanovsky, G., & Schwartz, R. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 37

Do Image Classifiers Generalize Across Time?

Shankar, V., Dave, A., Roelofs, R., Ramanan, D., Recht, B., & Schmidt, L. (2019) · arXiv

Advanced Neural Network ApplicationsAdversarial Robustness in Machine Learning

Cited by 37

Contrasting Contrastive Self-Supervised Representation Learning Pipelines

Kotar, K., Ilharco, G., Schmidt, L., Ehsani, K., & Mottaghi, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 28

Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved With Text

Zhu, W., Hessel, J., Awadalla, A., Gadre, S. Y., Dodge, J., Fang, A., Yu, Y., Schmidt, L., Wang, W. Y., & Choi, Y. (2023) · arXiv

Multimodal Machine Learning ApplicationsNatural Language Processing Techniques

Cited by 21

Why are Visually-Grounded Language Models Bad at Image Classification?

Zhang, Y., Unell, A., Wang, X., Ghosh, D., Su, Y., Schmidt, L., & Yeung-Levy, S. (2024) · Advances in Neural Information Processing Systems 37

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 20

Detecting clinical medication errors with AI enabled wearable cameras

Chan, J., Nsumba, S., Wortsman, M., Dave, A., Schmidt, L., Gollakota, S., & Michaelsen, K. (2024) · npj Digital Medicine

Patient Safety and Medication ErrorsArtificial Intelligence in Healthcare and Education

Cited by 18

Model Similarity Mitigates Test Set Overuse

Mania, H., Miller, J., Schmidt, L., Hardt, M., & Recht, B. (2019) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 11