The field of neural networks is rapidly evolving, with a focus on improving optimization and compression techniques. Recent developments have led to a greater understanding of the loss landscape, revealing that low-loss regions are often continuous and fully connected. This has significant implications for model generalization and has sparked new approaches to exploring the low-loss space. Another area of focus is on-device learning, which has the potential to reduce latency and improve energy efficiency. However, this is hindered by memory and computational constraints, driving the need for innovative solutions such as shortcut approaches and post-training compression algorithms. Noteworthy papers in this area include:

• Low-Loss Space in Neural Networks is Continuous and Fully Connected, which proposes a new algorithm for investigating low-loss paths in the full parameter space.
• DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing, which introduces a novel compression algorithm that achieves near floating point performance without retraining. These advances are pushing the boundaries of what is possible with neural networks, enabling the development of more efficient, effective, and scalable models.