Deep Learning

We present ProCerDCL, a query-efficient framework for black-box probabilistic verification. Given a fixed, sampleable input …

Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in …

Online continual learning (OCL) is an enabling technology for safety-critical autonomous embedded systems whose deployment environments …

Vision-Language Models (VLMs) are increasingly deployed in real-world applications. To ensure efficient inference, these deployments …

Autonomous robotic systems, such as autonomous vehicles, drones, and mobile robots, increasingly require on-device Deep Reinforcement …

Online continual learning (OCL) enables real-time adaptation to new data, making it crucial for dynamic robotic applications. However, …

Future intelligent robots are expected to process multiple inputs simultaneously (such as image and audio data) and generate multiple …

Safety-critical cyber-physical systems (CPS), such as autonomous vehicles and robots, increasingly rely on deep neural networks. While …

Code generation is increasingly critical for real-world applications. Still, diffusion-based large language models continue to struggle …

Instruction-based text editing is increasingly critical for real-world applications such as code editors (e.g., Cursor), but Large …