Difference between revisions of "Michael DeWeese"

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*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).
 
*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).
  
*38. P.R. Zulkowski and M.R. DeWeese. [[File:x.pdf]] Optimal protocols for slowly driven quantum systems. Physical Review E. 92(3):032113. (2015).
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*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).
  
 
*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).
 
*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).

Revision as of 20:44, 24 October 2015

Here is my short CV and below is my publication list. Most papers are available here as PDFs.


Publications:

  • 23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. Minimum Probability Flow Learning. Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).
  • 11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).
  • 5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).