Search Results for author:
Found 6 papers, 3 papers with code
Experimentally testable whole brain manifolds that recapitulate behavior
Thus, as a final validation of how well GMN captures essential dynamic information, we show that the artificially generated time series can be used as a training set to predict out-of-sample observed fly locomotion, as well as brain activity in out of sample withheld data not used in model building.
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks
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 high dimension inputs.
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution
Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework.
Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA
An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism.
The Preliminary Evaluation of a Hypervisor-based Virtualization Mechanism for Intel Optane DC Persistent Memory Module
Through experiments, we confirmed that even though a VM has only 1% of DRAM in its RAM, the performance degradation of the VM was drastically alleviated by memory mapping optimization.
Operating Systems Hardware Architecture Performance D.4; B.3
Perturbative GAN: GAN with Perturbation Layers
Perturbative GAN, which replaces convolution layers of existing convolutional GANs (DCGAN, WGAN-GP, BIGGAN, etc.)
