A winning strategy for deep neural networks

LLNL continues to make an impact at top machine learning conferences, even as much of the research staff works remotely during the COVID-19 pandemic. Postdoctoral researcher James Diffenderfer and computer scientist Bhavya Kailkhura, both from LLNL’s Center for Applied Scientific Computing, are co-authors on a paper—“Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning a Randomly Weighted Network”—that offers a novel and unconventional way to train deep neural networks. The paper is among the Lab’s acceptances to the International Conference on Learning Representations (ICLR 2021), which takes place May 3–7. Read more at LLNL Computing.