The current research landscape in the field is marked by a significant focus on efficiency and optimization in neural network training and model compression. Researchers are increasingly exploring the neural collapse phenomenon, leveraging it to guide training and regularization processes, thereby reducing the number of trainable parameters without compromising model accuracy. This approach is particularly promising for deep fully connected networks and transformer architectures, where neural collapse is observed across multiple layers. Additionally, advancements in model compression techniques, particularly those utilizing second-order curvature information, are being refined to address scalability issues in high-dimensional parameter spaces. These methods aim to maintain or even enhance model performance at high levels of sparsity, making large AI models more accessible to those with limited computational resources. Furthermore, optimization strategies like the covariance matrix adaptation evolution strategy (CMA-ES) are being adapted to handle low effective dimensionality, improving performance in high-dimensional black-box optimization problems. Overall, the field is moving towards more efficient, scalable, and robust solutions that push the boundaries of what is computationally feasible in AI.

Noteworthy papers include one that proposes novel training approaches leveraging neural collapse across multiple layers, significantly reducing parameter count while maintaining performance. Another paper introduces a new second-order pruning method that efficiently scales to high-dimensional spaces, achieving notable performance improvements at high sparsity levels.