Navdeep Jaitly, Ph.D.
Affiliations: | 2014 | Computer Science | University of Toronto, Toronto, ON, Canada |
Area:
machine learningGoogle:
"Navdeep Jaitly"Mean distance: 15.15 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Geoffrey E. Hinton | grad student | 2014 | University of Toronto | |
| (Exploring deep learning methods for discovering features in speech signals.) | ||||
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Publications
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| Busia A, Jaitly N. (2017) Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction F1000research. 6 |
| LaMarche BL, Crowell KL, Jaitly N, et al. (2013) MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis. Bmc Bioinformatics. 14: 49 |
| Hinton G, Deng L, Yu D, et al. (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups Ieee Signal Processing Magazine. 29: 82-97 |
| Umar A, Kang H, Timmermans AM, et al. (2009) Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Molecular & Cellular Proteomics : McP. 8: 1278-94 |
| Jaitly N, Mayampurath A, Littlefield K, et al. (2009) Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data. Bmc Bioinformatics. 10: 87 |
| Ansong C, Yoon H, Porwollik S, et al. (2009) Global systems-level analysis of Hfq and SmpB deletion mutants in Salmonella: implications for virulence and global protein translation. Plos One. 4: e4809 |
| Qian WJ, Liu T, Petyuk VA, et al. (2009) Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample. Journal of Proteome Research. 8: 290-9 |
| Tolmachev AV, Monroe ME, Purvine SO, et al. (2008) Characterization of strategies for obtaining confident identifications in bottom-up proteomics measurements using hybrid FTMS instruments. Analytical Chemistry. 80: 8514-25 |
| Ding J, Sorensen CM, Jaitly N, et al. (2008) Application of the accurate mass and time tag approach in studies of the human blood lipidome. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences. 871: 243-52 |
| Polpitiya AD, Qian WJ, Jaitly N, et al. (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics (Oxford, England). 24: 1556-8 |