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The success of neural networks comes hand in hand with a desire for more interpretability.
A generalized probability density function for double-bounded random processes
Ponnambalam Kumaraswamy. 1980 · 1980
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
Earlier work this paper cites.
Extracting rules from artificial neural networks with distributed representations
Sebastian Thrun. 1995 · 1995
Earlier work this paper cites.
Extracting tree-structured representations of trained networks
Mark Craven and Jude W Shavlik. 1996 · 1996
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The rectified gaussian distribution
Nicholas D. Socci, Daniel D. Lee, and H. Sebastian Seung. 1998 · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
MichaelI. Jordan, Zoubin Ghahramani, TommiS. Jaakkola, and LawrenceK. Saul. 1999 · 1999
Earlier work this paper cites.
A variational bayesian method for rectified factor analysis
Markus Harva and Ata Kaban. 2005 · 2005
Earlier work this paper cites.
Variational message passing
John Winn and Christopher M Bishop. 2005 · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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