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Jake Levinson

Jake A. Levinson

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Journal Articles

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2023
[j2]
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  dblp key:
  - journals/dcg/GillespieLP23
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  - https://dblp.org/rec/journals/dcg/GillespieLP23
Maria Gillespie, Jake Levinson, Kevin Purbhoo:
Schubert Curves in the Orthogonal Grassmannian. Discret. Comput. Geom. 69(4): 981-1039 (2023)
2019
[j1]
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  - journals/combinatorics/GillespieL19
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Maria Gillespie, Jake Levinson:
Axioms for Shifted Tableau Crystals. Electron. J. Comb. 26(2): 2 (2019)

Conference and Workshop Papers

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2022
[c2]
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  - electronic edition @ mlr.press (open access)
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  - conf/aistats/AdlamLP22
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Ben Adlam, Jake A. Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions. AISTATS 2022: 3434-3457
2020
[c1]
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  - electronic edition @ neurips.cc (open access)
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  - conf/nips/LevinsonECSKRM20
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Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia:
An Analysis of SVD for Deep Rotation Estimation. NeurIPS 2020

Informal and Other Publications

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2020
[i3]
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  - electronic edition @ arxiv.org (open access)
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  - journals/corr/abs-2006-14616
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Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia:
An Analysis of SVD for Deep Rotation Estimation. CoRR abs/2006.14616 (2020)
2019
[i2]
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  - journals/corr/abs-1906-03281
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Jake Levinson, Avneesh Sud, Ameesh Makadia:
Latent feature disentanglement for 3D meshes. CoRR abs/1906.03281 (2019)
[i1]
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  - journals/corr/abs-1912-00827
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Ben Adlam, Jake Levinson, Jeffrey Pennington:
A Random Matrix Perspective on Mixtures of Nonlinearities for Deep Learning. CoRR abs/1912.00827 (2019)

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