no code implementations • ICLR 2022 • Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Mahmut T. Kandemir, Anand Sivasubramaniam
To solve the performance degradation, we propose to apply $\text{{Global Server Corrections}}$ on the server to refine the locally learned models.
no code implementations • NeurIPS 2020 • Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut Kandemir
Sampling-based methods promise scalability improvements when paired with stochastic gradient descent in training Graph Convolutional Networks (GCNs).
no code implementations • 17 Feb 2020 • Michael Norris, Berkay Celik, Patrick McDaniel, Gang Tan, Prasanna Venkatesh, Shulin Zhao, Anand Sivasubramaniam
IoT devices are decentralized and deployed in un-stable environments, which causes them to be prone to various kinds of faults, such as device failure and network disruption.
Software Engineering Performance
no code implementations • 15 Nov 2017 • Avinash Achar, Venkatesh Sarangan, R Rohith, Anand Sivasubramaniam
We address the problem of travel time prediction in arterial roads using data sampled from probe vehicles.
