| Distributed cognitive radio network management via algorithms in probabilistic graphical models Y Liang, L Lai, J Halloran IEEE Journal on Selected Areas in Communications 29 (2), 338-348, 2011 | 20 | 2011 |
| Classification: Naive Bayes vs logistic regression J Halloran Univ. Hawaii, Available online: http://melodi. ee. washington. edu/~ halloj3 …, 2009 | 18 | 2009 |
| Dynamic bayesian network for accurate detection of peptides from tandem mass spectra JT Halloran, JA Bilmes, WS Noble Journal of proteome research 15 (8), 2749-2759, 2016 | 16 | 2016 |
| Learning peptide-spectrum alignment models for tandem mass spectrometry JT Halloran, JA Bilmes, WS Noble Uncertainty in artificial intelligence: proceedings of the... conference …, 2014 | 16 | 2014 |
| Distributed algorithm for collaborative detection in cognitive radio networks Y Liang, L Lai, J Halloran 2009 47th Annual Allerton Conference on Communication, Control, and …, 2009 | 11 | 2009 |
| Speeding up percolator JT Halloran, H Zhang, K Kara, C Renggli, M The, C Zhang, DM Rocke, ... Journal of proteome research 18 (9), 3353-3359, 2019 | 9 | 2019 |
| A matter of time: faster percolator analysis via efficient svm learning for large-scale proteomics JT Halloran, DM Rocke Journal of proteome research 17 (5), 1978-1982, 2018 | 9 | 2018 |
| Spectrum identification using a dynamic bayesian network model of tandem mass spectra AP Singh, J Halloran, JA Bilmes, K Kirchoff, WS Noble Uncertainty in artificial intelligence: proceedings of the... conference …, 2012 | 9 | 2012 |
| Gradients of generative models for improved discriminative analysis of tandem mass spectra JT Halloran, DM Rocke Advances in neural information processing systems 30, 2017 | 8 | 2017 |
| Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data JT Halloran, G Urban, D Rocke, P Baldi bioRxiv, 2020.11. 12.380881, 2020 | 5 | 2020 |
| Jensen: An easily-extensible c++ toolkit for production-level machine learning and convex optimization R Iyer, JT Halloran, K Wei arXiv preprint arXiv:1807.06574, 2018 | 4 | 2018 |
| Learning concave conditional likelihood models for improved analysis of tandem mass spectra JT Halloran, DM Rocke Advances in neural information processing systems 31, 2018 | 3 | 2018 |
| Analyzing tandem mass spectra using the drip toolkit: Training, searching, and post-processing JT Halloran Data Mining for Systems Biology: Methods and Protocols, 163-180, 2018 | 2 | 2018 |
| Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies J Liu, JT Halloran, JA Bilmes, RM Daza, C Lee, EM Mahen, D Prunkard, ... Scientific Reports 7 (1), 16943, 2017 | 2 | 2017 |
| Faster and more accurate graphical model identification of tandem mass spectra using trellises S Wang, JT Halloran, JA Bilmes, WS Noble Bioinformatics 32 (12), i322-i331, 2016 | 2 | 2016 |
| GPU-accelerated primal learning for extremely fast large-scale classification JT Halloran, DM Rocke Advances in Neural Information Processing Systems 33, 5945-5956, 2020 | 1 | 2020 |
| GPU-Accelerated SVM Learning for Extremely Fast Large-Scale Proteomics Analysis JT Halloran, DM Rocke Mlcb. Github. Io, 2019 | 1 | 2019 |
| Training HMMs in GMTK JT Halloran University of Washington, Seattle, 2015 | 1 | 2015 |
| GMTK Tutorial on Dynamic Graphical Model Training with Gaussian Mixture Unaries, using TIMIT R Rogers, JA Bilmes, JT Halloran University of Washington, Seattle, 2015 | 1 | 2015 |
| Faster graphical model identification of tandem mass spectra using peptide word lattices S Wang, JT Halloran, JA Bilmes, WS Noble arXiv preprint arXiv:1410.7875, 2014 | 1 | 2014 |
David M. RockeBiostatistics and Biomedical Engineering, University of California, DavisVerified email at ucdavis.edu
