Vladimir Braverman

most citedA framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments

4 citations · 5 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2023★ 4 cited

A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments

Guangyao Zheng, Shuhao Lai, Vladimir Braverman +2

While Deep Reinforcement Learning has been widely researched in medical imaging, the training and deployment of these models usually require powerful GPUs. Since imaging environmen…

cs.DS2014★ 1 cited

Universal sketches for the frequency negative moments and other decreasing streaming sums

Vladimir Braverman, Stephen R. Chestnut

Given a stream with frequencies $f_{d}$ , for $d \in [n]$ , we characterize the space necessary for approximating the frequency negative moments $F_{p} = \sum ∣ f_{d} ∣^{p}$ , where $p < 0$ and the s…