Jenatton, Rodolphe

Hyperparameter ensembles for robustness and uncertainty quantification

Wenzel, F., Snoek, J., Tran, D., & Jenatton, R. (2020) · arXiv

Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning

Cited by 122

Scaling vision transformers to 22 billion parameters

Dehghani, M., Djolonga, J., Mustafa, B., Padlewski, P., Heek, J., Gilmer, J., Steiner, A., Caron, M., Geirhos, R., Alabdulmohsin, I., et al. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 112

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Abstract LB174: H-optimus-1: A foundation model for computational histopathology

Scalbert, M., Saillard, C., Peeters, T., Gonzalez, L., Valter, D., Llinares-López, F., Mariet, Z. E., & Jenatton, R. (2026) · Cancer Research

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 3