Mueller, Jonas

ResNeSt: Split-Attention Networks

Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Lin, H., Zhang, Z., Sun, Y., He, T., Mueller, J., Manmatha, R., et al. (2020) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,809

Siamese Recurrent Architectures for Learning Sentence Similarity

Mueller, J., & Thyagarajan, A. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 935

Pervasive label errors in test sets destabilize machine learning benchmarks

Northcutt, C. G., Athalye, A., & Mueller, J. (2021) · arXiv

Machine Learning and Data ClassificationAdversarial Robustness in Machine Learning

Cited by 293

Antibody complementarity determining region design using high-capacity machine learning

Liu, G., Zeng, H., Mueller, J., Carter, B., Wang, Z., Schilz, J., Horny, G., Birnbaum, M. E., Ewert, S., & Gifford, D. K. (2020) · Bioinformatics

Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

Cited by 157

Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

Chen, J., & Mueller, J. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 33

Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles

Jain, S., Liu, G., Mueller, J., & Gifford, D. (2019) · arXiv

Adversarial Robustness in Machine LearningMachine Learning and Data Classification

Cited by 8

DataPerf: Benchmarks for Data-Centric AI Development

Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., et al. (2023) · Advances in Neural Information Processing Systems 36

Advanced Database Systems and QueriesScientific Computing and Data Management

Cited by 5