Miller, John

Pan genome of the phytoplankton Emiliania underpins its global distribution

Emiliania huxleyi Annotation Consortium, Read, B. A., Kegel, J., Klute, M. J., Kuo, A., Lefebvre, S. C., Maumus, F., Mayer, C., Miller, J., Monier, A., et al. (2013) · Nature

Protist diversity and phylogenyGenomics and Phylogenetic Studies

Cited by 516

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., & Hardt, M. (2019) · arXiv

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 393

Traversing Knowledge Graphs in Vector Space

Guu, K., Miller, J., & Liang, P. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Advanced Graph Neural NetworksTopic Modeling

Cited by 319

Deep voice 3: scaling text-to-speech with convolutional sequence learning

Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., & Miller, J. (2017) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 313

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

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