The field of neural networks is moving towards optimizing models for efficiency and interpretability. Researchers are exploring new methods to reduce the complexity of neural networks, making them more suitable for deployment in resource-constrained settings. This includes developing pruning strategies that conserve functional integrity, as well as compression schemes that reduce the number of computations required for inference. Another area of focus is improving the interpretability of neural networks, with techniques such as mixed-integer programming frameworks being used to train sparse and interpretable models. Additionally, there is a growing interest in adapting neural networks to specific tasks or domains, such as using low-rank adaptations to update neural fields. Notable papers in this area include:

• A paper that introduces a component-aware pruning strategy for multi-component neural architectures, achieving greater sparsity and reduced performance degradation.
• A paper that presents a unified mixed-integer programming framework for training sparse and interpretable neural networks, yielding globally optimal solutions with respect to a composite objective.
• A paper that proposes a parameter-efficient strategy for updating neural fields using low-rank adaptations, demonstrating its effectiveness and versatility for representing neural field updates.