Explores challenges in data center networks, introduces physical-layer programmability, innovative topologies, and practical implementations to enhance network reliability and performance.
Covers the Macroscopic Fundamental Diagram in traffic flow modeling, discussing its physical properties, regularity conditions, and congestion propagation.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
