Dahl, George E.

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 10,260

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 3,011

Improving deep neural networks for LVCSR using rectified linear units and dropout

Dahl, G. E., Sainath, T. N., & Hinton, G. E. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,310

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,159

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 651

Detecting Cancer Metastases on Gigapixel Pathology Images

Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv

AI in cancer detectionCell Image Analysis Techniques

Cited by 506

Measuring the Effects of Data Parallelism on Neural Network Training

Shallue, C. J., Lee, J., Antognini, J., Sohl-Dickstein, J., Frostig, R., & Dahl, G. E. (2018) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 229

Adaptive Gradient Methods at the Edge of Stability

Cohen, J. M., Ghorbani, B., Krishnan, S., Agarwal, N., Medapati, S., Badura, M., Suo, D., Cardoze, D., Nado, Z., Dahl, G. E., et al. (2022) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 97

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Gomes, R. G., Vwalika, B., Lee, C., Willis, A., Sieniek, M., Price, J. T., Chen, C., Kasaro, M. P., Taylor, J. A., Stringer, E. M., et al. (2022) · Communications Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 47