2 code implementations • NeurIPS 2020 • Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, A. Salman Avestimehr, Mahdi Soltanolkotabi
In this approach a model trained for a source task, where plenty of labeled training data is available, is used as a starting point for training a model on a related target task with only few labeled training data.
no code implementations • 19 Jan 2019 • Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, A. Salman Avestimehr
Perhaps unexpectedly, we show that QSGD maintains the fast convergence of SGD to a globally optimal model while significantly reducing the communication cost.
no code implementations • 4 Jun 2018 • Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Salman Avestimehr
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms.
no code implementations • 24 May 2018 • Songze Li, Seyed Mohammadreza Mousavi Kalan, Qian Yu, Mahdi Soltanolkotabi, A. Salman Avestimehr
In particular, PCR requires a recovery threshold that scales inversely proportionally with the amount of computation/storage available at each worker.
no code implementations • 31 Mar 2018 • A. Salman Avestimehr, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi
We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy), and straggler toleration in this framework.