5 citations · 7 across the 9 of their papers we have counts for
9 papers
QNNRepair: Quantized Neural Network Repair
Xidan Song, Youcheng Sun, Mustafa A. Mustafa +1
We present QNNRepair, the first method in the literature for repairing quantized neural networks (QNNs). QNNRepair aims to improve the accuracy of a neural network model after quan…
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
Mohamed Amine Ferrag, Othmane Friha, Burak Kantarci +6
The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE)…
Interventional Probing in High Dimensions: An NLI Case Study
Julia Rozanova, Marco Valentino, Lucas Cordeiro +1
Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the "natural log…
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study
Mohamed Amine Ferrag, Burak Kantarci, Lucas C. Cordeiro +2
Federated edge learning can be essential in supporting privacy-preserving, artificial intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) envir…
Montague semantics and modifier consistency measurement in neural language models
Danilo S. Carvalho, Edoardo Manino, Julia Rozanova +2
This work proposes a novel methodology for measuring compositional behavior in contemporary language embedding models. Specifically, we focus on adjectival modifier phenomena in ad…
AIREPAIR: A Repair Platform for Neural Networks
Xidan Song, Youcheng Sun, Mustafa A. Mustafa +1
We present AIREPAIR, a platform for repairing neural networks. It features the integration of existing network repair tools. Based on AIREPAIR, one can run different repair methods…
Towards Global Neural Network Abstractions with Locally-Exact Reconstruction
Edoardo Manino, Iury Bessa, Lucas Cordeiro
Neural networks are a powerful class of non-linear functions. However, their black-box nature makes it difficult to explain their behaviour and certify their safety. Abstraction te…
Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
Edoardo Manino, Julia Rozanova, Danilo Carvalho +2
Metamorphic testing has recently been used to check the safety of neural NLP models. Its main advantage is that it does not rely on a ground truth to generate test cases. However,…
Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical Approach
Kamil Erdayandi, Amrit Paudel, Lucas Cordeiro +1
In this paper, we propose a decentralized, privacy-friendly energy trading platform (PFET) based on game theoretical approach - specifically Stackelberg competition. Unlike existin…