12 citations · 27 across the 7 of their papers we have counts for
12 papers
Experimentally testable whole brain manifolds that recapitulate behavior
Gerald M Pao, Cameron Smith, Joseph Park +8
We propose an algorithm grounded in dynamical systems theory that generalizes manifold learning from a global state representation, to a network of local interacting manifolds term…
kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos
Keichi Takahashi, Wassapon Watanakeesuntorn, Kohei Ichikawa +5
Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series analysis framework. Despite its wide applicability, EDM was not scalable to large datasets due to its…
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks
Albert Njoroge Kahira, Truong Thao Nguyen, Leonardo Bautista Gomez +3
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence and alleviate memory capacity limitations when training large models and/or using…
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
Wassapon Watanakeesuntorn, Keichi Takahashi, Kohei Ichikawa +5
Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to compu…
Disaggregated Accelerator Management System for Cloud Data Centers
Ryousei Takano, Kuniyasu Suzaki
A conventional data center that consists of monolithic-servers is confronted with limitations including lack of operational flexibility, low resource utilization, low maintainabili…
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
Mohamed Wahib, Haoyu Zhang, Truong Thao Nguyen +5
The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a via…