[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Software engineering for AI-based systems: a survey
AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …
Deephunter: a coverage-guided fuzz testing framework for deep neural networks
The past decade has seen the great potential of applying deep neural network (DNN) based
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …
Retrieval-augmented generation for code summarization via hybrid gnn
Source code summarization aims to generate natural language summaries from structured
code snippets for better understanding code functionalities. However, automatic code …
code snippets for better understanding code functionalities. However, automatic code …
Wuji: Automatic online combat game testing using evolutionary deep reinforcement learning
Game testing has been long recognized as a notoriously challenging task, which mainly
relies on manual playing and scripting based testing in game industry. Even until recently …
relies on manual playing and scripting based testing in game industry. Even until recently …
Deepstellar: Model-based quantitative analysis of stateful deep learning systems
Deep Learning (DL) has achieved tremendous success in many cutting-edge applications.
However, the state-of-the-art DL systems still suffer from quality issues. While some recent …
However, the state-of-the-art DL systems still suffer from quality issues. While some recent …
An empirical study of common challenges in developing deep learning applications
Recent advances in deep learning promote the innovation of many intelligent systems and
applications such as autonomous driving and image recognition. Despite enormous efforts …
applications such as autonomous driving and image recognition. Despite enormous efforts …
Efficientderain: Learning pixel-wise dilation filtering for high-efficiency single-image deraining
Single-image deraining is rather challenging due to the unknown rain model. Existing
methods often make specific assumptions of the rain model, which can hardly cover many …
methods often make specific assumptions of the rain model, which can hardly cover many …
A performance-sensitive malware detection system using deep learning on mobile devices
Currently, Android malware detection is mostly performed on server side against the
increasing number of malware. Powerful computing resource provides more exhaustive …
increasing number of malware. Powerful computing resource provides more exhaustive …
An empirical study towards characterizing deep learning development and deployment across different frameworks and platforms
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks
and platforms play a key role to catalyze such progress. However, the differences in …
and platforms play a key role to catalyze such progress. However, the differences in …