A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …
in achieving human-level performance on several long-standing tasks. With the broader …
Algorithms for verifying deep neural networks
Deep neural networks are widely used for nonlinear function approximation, with
applications ranging from computer vision to control. Although these networks involve the …
applications ranging from computer vision to control. Although these networks involve the …
Machine learning testing: Survey, landscapes and horizons
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
A survey of safety and trustworthiness of large language models through the lens of verification and validation
Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …
engage end-users in human-level conversations with detailed and articulate answers across …
Deepgauge: Multi-granularity testing criteria for deep learning systems
Deep learning (DL) defines a new data-driven programming paradigm that constructs the
internal system logic of a crafted neuron network through a set of training data. We have …
internal system logic of a crafted neuron network through a set of training data. We have …
Testing machine learning based systems: a systematic mapping
Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …
one or more components that learn how to perform a task from a given data set. The …
A review of deep learning with special emphasis on architectures, applications and recent trends
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
Deepmutation: Mutation testing of deep learning systems
Deep learning (DL) defines a new data-driven programming paradigm where the internal
system logic is largely shaped by the training data. The standard way of evaluating DL …
system logic is largely shaped by the training data. The standard way of evaluating DL …
Tensorfuzz: Debugging neural networks with coverage-guided fuzzing
Neural networks are difficult to interpret and debug. We introduce testing techniques for
neural networks that can discover errors occurring only for rare inputs. Specifically, we …
neural networks that can discover errors occurring only for rare inputs. Specifically, we …
Concolic testing for deep neural networks
Concolic testing combines program execution and symbolic analysis to explore the
execution paths of a software program. In this paper, we develop the first concolic testing …
execution paths of a software program. In this paper, we develop the first concolic testing …