15 citations · 20 across the 2 of their papers we have counts for
3 papers
Differential Equation Units: Learning Functional Forms of Activation Functions from Data
MohamadAli Torkamani, Shiv Shankar, Amirmohammad Rooshenas +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions
MohamadAli Torkamani, Phillip Wallis, Shiv Shankar +1
Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential e…
The Libra Toolkit for Probabilistic Models
Daniel Lowd, Amirmohammad Rooshenas
The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, an…