Search Results for author: Colin Paterson

Found 5 papers, 1 papers with code

Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

no code implementations19 Mar 2021 Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei

Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.

BIG-bench Machine Learning

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers

no code implementations2 Mar 2021 Colin Paterson, Haoze Wu, John Grese, Radu Calinescu, Corina S. Pasareanu, Clark Barrett

We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast.

Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)

1 code implementation2 Feb 2021 Richard Hawkins, Colin Paterson, Chiara Picardi, Yan Jia, Radu Calinescu, Ibrahim Habli

Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance.

BIG-bench Machine Learning

Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers

no code implementations28 Nov 2019 Colin Paterson, Radu Calinescu, Chiara Picardi

Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems.

Decision Making

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

no code implementations10 May 2019 Rob Ashmore, Radu Calinescu, Colin Paterson

Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i. e. in the generation of evidence that ML is sufficiently safe for its intended use.

BIG-bench Machine Learning

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