6 papers
PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training
Sattam Altuuaim, Lama Ayash, Muhammad Mubashar +1
Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can l…
Epistemic Generative Adversarial Networks
Muhammad Mubashar, Fabio Cuzzolin
Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range…
Epistemic Wrapping for Uncertainty Quantification
Maryam Sultana, Neil Yorke-Smith, Kaizheng Wang +3
Uncertainty estimation is pivotal in machine learning, especially for classification tasks, as it improves the robustness and reliability of models. We introduce a novel `Epistemic…
Random-Set Large Language Models
Muhammad Mubashar, Shireen Kudukkil Manchingal, Fabio Cuzzolin
Large Language Models (LLMs) are known to produce very high-quality tests and responses to our queries. But how much can we trust this generated text? In this paper, we study the p…
A Unified Evaluation Framework for Epistemic Predictions
Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang +1
Predictions of uncertainty-aware models are diverse, ranging from single point estimates (often averaged over prediction samples) to predictive distributions, to set-valued or cred…
Random-Set Neural Networks (RS-NN)
Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang +2
Machine learning is increasingly deployed in safety-critical domains where erroneous predictions may lead to potentially catastrophic consequences, highlighting the need for learni…