2 citations · 6 across the 5 of their papers we have counts for
6 papers
Automatically Evolving Prompt Guidelines for Task-Specific Optimization
Cedric Richter, Salah Ghamizi, Mike Papadakis
For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints. In practice, however, user queries are often underspec…
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Salah Ghamizi, Maxime Cordy, Yuejun Guo +2
Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number o…
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Salah Ghamizi, Maxime Cordy, Mike Papadakis +1
Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets,…
Data-driven Simulation and Optimization for Covid-19 Exit Strategies
Salah Ghamizi, Renaud Rwemalika, Lisa Veiber +5
The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those meas…
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique
Salah Ghamizi, Maxime Cordy, Mike Papadakis +1
We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural netw…
Automated Search for Configurations of Deep Neural Network Architectures
Salah Ghamizi, Maxime Cordy, Mike Papadakis +1
Deep Neural Networks (DNNs) are intensively used to solve a wide variety of complex problems. Although powerful, such systems require manual configuration and tuning. To this end,…