Dynamic Temperature Scheduler for Knowledge Distillation

The recent introduction of NOVAK, a unified adaptive optimizer for deep neural networks, combines several advanced techniques including adaptive moment estimation and lookahead synchronization, aiming to enhance the performance and efficiency of neural network training.

A recent study has introduced a closed-loop framework for Neural Architecture Search (NAS) utilizing Large Language Models (LLMs) to optimize channel configurations in vision models. This approach addresses the combinatorial challenges of layer specifications in deep neural networks by leveraging LLMs to generate and refine architectural designs based on performance data.

A new framework named InfGraND has been introduced to facilitate Influence-guided Knowledge Distillation from Graph Neural Networks (GNNs) to Multi-Layer Perceptrons (MLPs). This framework aims to enhance the efficiency of MLPs by prioritizing structurally influential nodes in the graph, addressing challenges faced by traditional GNNs in low-latency and resource-constrained environments.