The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

Channel pruning is a class of powerful methods for model compression. When pruning a
neural network, it's ideal to obtain a sub-network with higher accuracy. However, a sub …

The success of the upcoming 6G systems will largely depend on the quality of the Network
Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) …

Recent methods for long-tailed instance segmentation still struggle on rare object classes
with few training data. We propose a simple yet effective method, Feature Augmentation and …

We propose a meta-learning technique for offline discovery of physics-informed neural
network (PINN) loss functions. We extend earlier works on meta-learning, and develop a …

AI practitioners typically strive to develop the most accurate systems, making an implicit
assumption that the AI system will function autonomously. However, in practice, AI systems …

Arguments that machine learning (ML) is facing a reproducibility and replication crisis
suggest that some published claims in research cannot be taken at face value. Concerns …

As the size of turbulent flow simulations continues to grow, in situ data compression is
becoming increasingly important for visualization, analysis, and restart checkpointing. For …

A popular line of recent research incorporates ML advice in the design of online algorithms
to improve their performance in typical instances. These papers treat the ML algorithm as a …

Adaptive loss function formulation is an active area of research and has gained a great deal
of popularity in recent years, following the success of deep learning. However, existing …