- (2020) C.B. Scott and Eric Mjolsness, “Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton”. Machine Learning: Science and Technology 2 015009, 1 December 2020. https://iopscience.iop.org/article/10.1088/2632-2153/abb6d2
- (2019) Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness, “Learning Moment Closure Approximations in Reaction-Diffusion Systems with Spatial Dynamic Boltzmann Distributions”. Physical Review E, v.99, 063315, 26 June 2019. DOI: 10.1103/PhysRevE.99.063315 .
- (2019) Cory Scott and Eric Mjolsness, “Multilevel Artificial Neural Network Training for Spatially Correlated Learning”. SIAM Journal on Scientific Computing, accepted 22 April 2019.
- (2018) Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, and Eric Mjolsness, “Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics”. Journal of Chemical Physics 149, 034107, July 2018. Also arXiv 1803.01063, March 2018.
- (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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