arXiv:2511.20702v1 Announce Type: new Abstract: Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on…
arXiv:2506.04263v2 Announce Type: replace-cross Abstract: Adversarial training is among the most effective strategies for defending deep neural networks against adversarial examples. A key limitation of existing adversarial training approaches lies in their reliance on a fixed perturbation budget, which fails…
arXiv:2511.20729v1 Announce Type: new Abstract: Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have…
arXiv:2511.20779v1 Announce Type: new Abstract: Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human experts during inference. This work proposes the Calibrated…
arXiv:2511.21600v1 Announce Type: cross Abstract: The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising…
arXiv:2511.20798v1 Announce Type: new Abstract: Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be…
arXiv:2406.19622v2 Announce Type: replace Abstract: As deep neural networks (DNNs) are increasingly deployed in sensitive applications, ensuring their security and robustness has become critical. A major threat to DNNs arises from adversarial attacks, where small input perturbations can lead to…
arXiv:2511.20811v1 Announce Type: new Abstract: We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need…
arXiv:2505.18636v2 Announce Type: replace Abstract: The go-to strategy to apply deep networks in settings where uncertainty informs decisions–ensembling multiple training runs with random initializations–is ill-suited for the extremely large-scale models and practical fine-tuning workflows of today. We introduce a new…
arXiv:2511.20826v1 Announce Type: new Abstract: Untrained large neural networks, just after random initialization, tend to favour a small subset of classes, assigning high predicted probabilities to these few classes and approximately zero probability to all others. This bias, termed Initial…
