Uncertainty Aware Deployment

Uncertainty-aware deployment focuses on reliably deploying machine learning models, particularly deep learning models like transformers and large language models (LLMs), in real-world applications where conditions may deviate from training data. Current research emphasizes techniques like mixed-precision quantization, contrastive learning, and Bayesian methods to improve model efficiency and robustness while quantifying and managing uncertainty. This work is crucial for bridging the gap between successful model training and dependable real-world performance across diverse hardware platforms and application domains, including robotics, edge computing, and wireless communication.