- (2015) Todd Johnson, Thomas Bartol, Terrence Sejnowski, and Eric Mjolsness. “Model Reduction for Stochastic CaMKII Reaction Kinetics in Synapses by Graph-Constrained Correlation Dynamics”. Physical Biology 12:4, July 2015.
- (2012) Eric Mjolsness, Compositional stochastic modeling and probabilistic programming. Workshop on Probabilistic Programming, Neural Information Processing Systems Conference Workshops, extended abstract, December 2012. [Mjolsness_1212.0582] Also available as [arXiv:1212.0582]
- (2010) Wang, Y., Christley, S., Mjolsness, E., and Xie, X. Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent , BMC Systems Biology 4:99 . [ Published PDF ]
- (2003) Clustering analysis of microarray gene expression data by splitting algorithm . Ruye Wang, Lucas Scharenbroich, Christopher Hart, Barbara Wold, and Eric Mjolsness. Journal of Parallel and Distributed Computing, Volume 63, Numbers 7-8, pp. 692-706, July-August 2003. [ Preprint ]
- (2001) Machine learning for science: State of the art and future prospects. Eric Mjolsness and Dennis DeCoste, Science 293, 2051-2055, September 14, 2001. [ Paper ]
- (1999) From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Expression Data. E. Mjolsness, T. Mann, R. Castaño, and B. Wold. Advances in Neural Information Processing Systems 1999. [Paper]
- (1996) Learning with preknowledge: Clustering with point and graph matching distance measures. Steven Gold, Anand Rangarajan, and Eric Mjolsness, Neural Computation , vol 8 no 4, May 15 1996. Reprinted in Unsupervised Learning: Foundations of Neural Computation , eds. G. Hinton and T. J. Sejnowski, MIT Press 1999. [Journal paper | PDF Preprint | Postscript Preprint ]
- (1994) Clustering with a Domain-Specific Distance Measure. Steven Gold, Anand Rangarajan, and Eric Mjolsness, Advances in Neural Information Processing Systems 6 , editors Cowan, Tesauro, Alspector, Morgan-Kaufmann 1994. [ Preprint ]
- (1989) Scaling, machine learning, and genetic neural nets, Eric Mjolsness, David H. Sharp, and Bradley K. Alpert. Advances in Applied Mathematics, June 1989. [ Paper ]
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