John T. Halloran

Affiliations:
  • University of California, Davis, Department of Public Health Sciences, CA, USA
  • University of Washington, Department of Electrical Engineering, Seattle, WA, USA


According to our database1, John T. Halloran authored at least 9 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Mamba State-Space Models Can Be Strong Downstream Learners.
CoRR, 2024

2020
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2018
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization.
CoRR, 2018

Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Analyzing Tandem Mass Spectra: A Graphical Models Perspective.
Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

2016
Faster and more accurate graphical model identification of tandem mass spectra using trellises.
Bioinform., 2016

2014
Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

2012
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012


  Loading...