Efficient representation of Recurrent Neural Networks for markovian/non-markovian non-linear control problems

2010 ◽  
Author(s):  
Maryam Mahsal Khan ◽  
Gul Muhammad Khan ◽  
Julian F. Miller
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Control ◽  
Control Problems ◽  
Non Linear ◽  
Efficient Representation

scholarly journals Scalable Polyhedral Verification of Recurrent Neural Networks

2021 ◽  
pp. 225-248
Author(s):  
Wonryong Ryou ◽  
Jiayu Chen ◽  
Mislav Balunovic ◽  
Gagandeep Singh ◽  
Andrei Dan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Networks ◽  
Sensor Data ◽  
Motion Sensor ◽  
Use Case ◽  
Prior Work ◽  
Non Linear ◽  
Speech Classification ◽  
Sampling Optimization

AbstractWe present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

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