Michael DeWeese: Difference between revisions

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*32. J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/2/2b/SohlDickstein_Mudigonda_DeWeese_Sampling_Without_Detailed_Ballance_preprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).
*32. J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/2/2b/SohlDickstein_Mudigonda_DeWeese_Sampling_Without_Detailed_Ballance_preprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).


*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE (in press)
*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE 8(12):e82754 (2013).


*30.  J. Zylberberg and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).
*30.  J. Zylberberg and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).

Revision as of 07:27, 27 December 2013

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

Selected manuscripts in preparation:

  • j. P.R. Zulkowski and M.R. DeWeese. Optimal entropy production. (in preparation)
  • h. M. Leonard and M.R. DeWeese. A subpopulation of neurons in prefrontal cortex encode recent actions in a working memory task but only during uncued trials. (in preparation)
  • g. N. Carlson, V.L. Ming, and M.R. DeWeese. Probe stimuli affect receptive field estimation of model auditory neurons optimized to represent speech efficiently. (in preparation)
  • e. S. Corinaldi and M.R. DeWeese. A network model of task switching optimized to minimize errors predicts several counterintuitive features of human behavioral data. (in preparation).

Submitted manuscripts:

All 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).