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Probabilistic symmetries and invariant neural networks Treating neural network inputs and outputs as random variables, we characterize the structure of neural networks that can be used to model data that are invariant or equivariant under the action of a compact group
Probabilistic Symmetries and Invariant Neural Networks Our representations completely characterize the structure of neural networks that can be used to model such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks
Benjamin Bloem-Reddy - Google Scholar Co-authors View all Yee Whye Teh Professor of Statistical Machine Learning, Oxford, Research Director, Google DeepMind Johnny Xi Graduate Student, University of British Columbia Mark van der
Probabilistic symmetry and invariant neural networks Deep neural networks have been applied successfully in a range of settings Effort under way to improve performance in data poor and semi- unsupervised domains Focus on symmetry The study of symmetry in probability and statistics has a long history For input X and output Y, model Y network
Probabilistic symmetries and invariant neural networks | 集智斑图 Our representations completely characterize the structure of neural networks that can be used to model such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks
jmlr. org @article{JMLR:v21:19-322, author = {Benjamin Bloem-Reddy and { Yee Whye } Teh}, title = {Probabilistic Symmetries and Invariant Neural Networks}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {90}, pages = {1--61}, url = {http: jmlr org papers v21 19-322 html} }
Probabilistic symmetries and invariant neural networks Our representations completely characterize the structure of neural networks that can be used to model such distributions and yield a general program for constructing invariant stochastic or deterministic neural networks
Benjamin Bloem-Reddy - dblp CoRR abs 2106 10800 (2021) 2020 [j1] Benjamin Bloem-Reddy, Yee Whye Teh: Probabilistic Symmetries and Invariant Neural Networks J Mach Learn Res 21: 90:1-90:61 (2020) [i6] Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy: On the Benefits of Invariance in Neural Networks CoRR abs 2005 00178 (2020) [i5]
Probabilistic symmetries and invariant neural networks Article "Probabilistic symmetries and invariant neural networks" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as "JST")