102 citations · 102 across the 4 of their papers we have counts for
4 papers
DistShap: Scalable GNN Explanations with Distributed Shapley Values
Selahattin Akkas, Aditya Devarakonda, Ariful Azad
With the growing adoption of graph neural networks (GNNs), explaining their predictions has become increasingly important. However, attributing predictions to specific edges or fea…
Communication-Efficient, 2D Parallel Stochastic Gradient Descent for Distributed-Memory Optimization
Aditya Devarakonda, Ramakrishnan Kannan
Distributed-memory implementations of numerical optimization algorithm, such as stochastic gradient descent (SGD), require interprocessor communication at every iteration of the al…
Sequential and Shared-Memory Parallel Algorithms for Partitioned Local Depths
Aditya Devarakonda, Grey Ballard
In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD). Given a set of data points and pairwise distanc…
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Aditya Devarakonda, Maxim Naumov, Michael Garland
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size. While smaller batch sizes generally c…