59 citations · 183 across the 18 of their papers we have counts for
6 papers · 1 filter
Prophecy: Inferring Formal Properties from Neuron Activations
Divya Gopinath, Corina S. Pasareanu, Muhammad Usman
We present Prophecy, a tool for automatically inferring formal properties of feed-forward neural networks. Prophecy is based on the observation that a significant part of the logic…
QuantifyML: How Good is my Machine Learning Model?
Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu
The efficacy of machine learning models is typically determined by computing their accuracy on test data sets. However, this may often be misleading, since the test data may not be…
NNrepair: Constraint-based Repair of Neural Network Classifiers
Muhammad Usman, Divya Gopinath, Youcheng Sun +2
We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the la…
NEUROSPF: A tool for the Symbolic Analysis of Neural Networks
Muhammad Usman, Yannic Noller, Corina Pasareanu +2
This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks. Given a trained neural network model, the tool extracts the architecture and model parameters and…
Fast, Structured Clinical Documentation via Contextual Autocomplete
Divya Gopinath, Monica Agrawal, Luke Murray +3
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical co…
Property Inference for Deep Neural Networks
Divya Gopinath, Hayes Converse, Corina S. Pasareanu +1
We present techniques for automatically inferring formal properties of feed-forward neural networks. We observe that a significant part (if not all) of the logic of feed forward ne…