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2020 – today
- 2023
[j13]James B. Simon, Madeline Dickens, Dhruva Karkada, Michael Robert DeWeese:
The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)- 2022
- [c5]James Benjamin Simon, Sajant Anand, Michael Robert DeWeese:
Reverse Engineering the Neural Tangent Kernel. ICML 2022: 20215-20231 - 2021
- [j12]Charles G. Frye, James Simon, Neha S. Wadia, Andrew Ligeralde, Michael Robert DeWeese, Kristofer E. Bouchard:
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses. Neural Comput. 33(6): 1469-1497 (2021) - [i9]James B. Simon, Sajant Anand, Michael Robert DeWeese:
Reverse Engineering the Neural Tangent Kernel. CoRR abs/2106.03186 (2021) - [i8]James B. Simon, Madeline Dickens, Michael Robert DeWeese:
Neural Tangent Kernel Eigenvalues Accurately Predict Generalization. CoRR abs/2110.03922 (2021) - 2020
- [j11]Pratik S. Sachdeva, Jesse A. Livezey, Michael Robert DeWeese:
Heterogeneous Synaptic Weighting Improves Neural Coding in the Presence of Common Noise. Neural Comput. 32(7): 1239-1276 (2020) - [j10]Thomas E. Yerxa
, Eric Kee, Michael Robert DeWeese, Emily A. Cooper:
Efficient sensory coding of multidimensional stimuli. PLoS Comput. Biol. 16(9) (2020) - [i7]Michael Y.-S. Fang, Sasikanth Manipatruni, Casimir Wierzynski, Amir Khosrowshahi, Michael Robert DeWeese:
Design of optical neural networks with component imprecisions. CoRR abs/2001.01681 (2020) - [i6]Charles G. Frye, James Simon, Neha S. Wadia, Andrew Ligeralde, Michael Robert DeWeese, Kristofer E. Bouchard:
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses. CoRR abs/2003.10397 (2020) - [i5]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
A new method for parameter estimation in probabilistic models: Minimum probability flow. CoRR abs/2007.09240 (2020)
2010 – 2019
- 2019
- [j9]Eric McVoy Dodds, Michael Robert DeWeese:
On the Sparse Structure of Natural Sounds and Natural Images: Similarities, Differences, and Implications for Neural Coding. Frontiers Comput. Neurosci. 13: 39 (2019) - [i4]Charles G. Frye, Neha S. Wadia, Michael Robert DeWeese, Kristofer E. Bouchard:
Numerically Recovering the Critical Points of a Deep Linear Autoencoder. CoRR abs/1901.10603 (2019) - [i3]Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander A. Alemi, Lee Altenberg, Gavin E. Crooks, James P. Crutchfield, Lídia del Rio, Josh Deutsch, Michael Robert DeWeese, Khari Douglas, Massimiliano Esposito, Michael P. Frank, Robert Fry, Peter Harsha, Mark D. Hill, Christopher T. Kello, Jeff Krichmar, Suhas Kumar, Shih-Chii Liu, Seth Lloyd, Matteo Marsili, Ilya Nemenman, Alex Nugent, Norman H. Packard, Dana Randall, Peter Sadowski, Narayana Santhanam, Robert Shaw, Adam Z. Stieg, Elan Stopnitzky, Christof Teuscher, Chris Watkins, David H. Wolpert, J. Joshua Yang, Yan Yufik:
Thermodynamic Computing. CoRR abs/1911.01968 (2019) - 2017
- [j8]Badr F. Albanna, Christopher Hillar, Jascha Sohl-Dickstein, Michael Robert DeWeese:
Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations. Entropy 19(8): 427 (2017) - 2015
- [j7]James P. Crutchfield, Michael Robert DeWeese, Sarah E. Marzen:
Time resolution dependence of information measures for spiking neurons: scaling and universality. Frontiers Comput. Neurosci. 9: 105 (2015) - [j6]Jascha Sohl-Dickstein, Santani Teng, Benjamin M. Gaub, Chris C. Rodgers, Crystal Li, Michael Robert DeWeese, Nicol S. Harper:
A Device for Human Ultrasonic Echolocation. IEEE Trans. Biomed. Eng. 62(6): 1526-1534 (2015) - [i2]Sarah E. Marzen, Michael Robert DeWeese, James P. Crutchfield:
Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality. CoRR abs/1504.04756 (2015) - 2014
- [c4]Jascha Sohl-Dickstein, Mayur Mudigonda, Michael Robert DeWeese:
Hamiltonian Monte Carlo Without Detailed Balance. ICML 2014: 719-726 - 2013
- [j5]Joel Zylberberg, Michael Robert DeWeese:
Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images. PLoS Comput. Biol. 9(8) (2013) - 2012
- [j4]Nicole L. Carlson, Vivienne L. Ming, Michael Robert DeWeese:
Sparse Codes for Speech Predict Spectrotemporal Receptive Fields in the Inferior Colliculus. PLoS Comput. Biol. 8(7) (2012) - 2011
- [j3]Joel Zylberberg, Michael Robert DeWeese:
How Should Prey Animals Respond to Uncertain Threats? Frontiers Comput. Neurosci. 5: 20 (2011) - [j2]Joel Zylberberg, Jason Timothy Murphy, Michael Robert DeWeese:
A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields. PLoS Comput. Biol. 7(10) (2011) - [c3]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
Minimum Probability Flow Learning. ICML 2011: 905-912
2000 – 2009
- 2009
- [i1]Jascha Sohl-Dickstein, Peter Battaglino, Michael Robert DeWeese:
Minimum Probability Flow Learning. CoRR abs/0906.4779 (2009) - 2002
- [c2]Michael Robert DeWeese, Anthony M. Zador:
Binary Coding in Auditory Cortex. NIPS 2002: 101-108
1990 – 1999
- 1998
- [j1]Michael Robert DeWeese, Anthony M. Zador:
Asymmetric Dynamics in Optimal Variance Adaptation. Neural Comput. 10(5): 1179-1202 (1998) - 1995
- [c1]Michael Robert DeWeese:
Optimization Principles for the Neural Code. NIPS 1995: 281-287
Coauthor Index
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