Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

The emergence of generative artificial intelligence (AI) and large language models (LLMs)
has marked a new era of natural language processing (NLP), introducing unprecedented …

Abstract Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent
scalability in Natural Language Processing. In Computer Vision, however, almost all …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and beyond …

Abstract Sparsely activated Mixture-of-Experts (MoE) is becoming a promising paradigm for
multi-task learning (MTL). Instead of compressing multiple tasks' knowledge into a single …

Deep learning models have shown their advantage in many different tasks, including
neuroimage analysis. However, to effectively train a high-quality deep learning model, the …

Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often
lets those tasks learn better jointly. Multi-tasking models have become successful and often …

Perception systems in modern autonomous driving vehicles typically take inputs from
complementary multi-modal sensors, eg, LiDAR and cameras. However, in real-world …

Abstract Domain generalizable (DG) person re-identification (ReID) is a challenging
problem because we cannot access any unseen target domain data during training. Almost …