31 citations · 45 across the 10 of their papers we have counts for
10 papers
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
Hongsheng Hu, Xuyun Zhang, Zoran Salcic +3
Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting…
Neuromodulation Gated Transformer
Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado +4
We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We…
Large Language Models Are Not Strong Abstract Reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock +1
Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However,…
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
Vithya Yogarajan, Gillian Dobbie, Henry Gouk
An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and…
Disentanglement of Latent Representations via Causal Interventions
Gaël Gendron, Michael Witbrock, Gillian Dobbie
The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the di…
A Survey of Methods, Challenges and Perspectives in Causality
Gaël Gendron, Michael Witbrock, Gillian Dobbie
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their…
Membership Inference via Backdooring
Hongsheng Hu, Zoran Salcic, Gillian Dobbie +3
Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten. In the context of machine learning, this requir…
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Xinglong Chang, Katharina Dost, Kaiqi Zhao +4
Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential a…
Membership Inference Attacks on Machine Learning: A Survey
Hongsheng Hu, Zoran Salcic, Lichao Sun +3
Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However,…
Measuring the Quality of B Abstract Machines with ISO/IEC 25010
Cheng-Hao Cai, Jing Sun, Gillian Dobbie
The B method has facilitated the development of software by specifying the design of software as abstract machines and formally verifying the correctness of the abstract machines.…