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Nicholas P. Baskerville

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2020 – today

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2024
[j1]
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  - journals/jmlr/GranziolBWA024
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Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts:
Iterate Averaging in the Quest for Best Test Error. J. Mach. Learn. Res. 25: 20:1-20:55 (2024)
2023
[i6]
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  - journals/corr/abs-2306-02108
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Nicholas P. Baskerville:
Random matrix theory and the loss surfaces of neural networks. CoRR abs/2306.02108 (2023)
2022
[i5]
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  - journals/corr/abs-2203-08061
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Nicholas P. Baskerville:
A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration. CoRR abs/2203.08061 (2022)
[i4]
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  - journals/corr/abs-2205-08601
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Nicholas P. Baskerville, Jonathan P. Keating, Francesco Mezzadri, Joseph Najnudel, Diego Granziol:
Universal characteristics of deep neural network loss surfaces from random matrix theory. CoRR abs/2205.08601 (2022)
2021
[i3]
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  - journals/corr/abs-2101-02524
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Nicholas P. Baskerville, Jonathan P. Keating, Francesco Mezzadri, Joseph Najnudel:
A spin-glass model for the loss surfaces of generative adversarial networks. CoRR abs/2101.02524 (2021)
[i2]
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  - journals/corr/abs-2102-06740
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Nicholas P. Baskerville, Diego Granziol, Jonathan P. Keating:
Applicability of Random Matrix Theory in Deep Learning. CoRR abs/2102.06740 (2021)
2020
[i1]
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  - journals/corr/abs-2004-03959
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Nicholas P. Baskerville, Jonathan P. Keating, Francesco Mezzadri, Joseph Najnudel:
The Loss Surfaces of Neural Networks with General Activation Functions. CoRR abs/2004.03959 (2020)

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