Set Coverage Analysis Tool for GCov
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Updated
Jun 20, 2016 - Python
Set Coverage Analysis Tool for GCov
Framework designed to benchmark Large-Scale Distributed Systems
LASSIE is a black-box deterministic simulator of large-scale mass-action biochemical systems
Data Mining Projects 2017
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The modelling environment (Atom3) used to develop LSMAS organisational meta-model for ModelMMORPG project at AI Lab @ FOI, and the meta-model being developed, along with a couple of models.
in-memory LInear large-scale GAzetteers - Standalone contain extraction tools for Natural Language Proces
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Extension of the ScheduleFlow Simulator to allow speculative request times at submission and during backfill
To alter mysql table with minimum downtime
This is the official development repository for BaseVar, which call variants for large-scale ultra low-pass (<1.0x) WGS data, especially for NIPT data
This is the design of a large-scale Banner Serving Service.
Pytorch implementation of "Large-Scale Meta-Learning with Continual Trajectory Shifting" (ICML 2021)
[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.
We present UDP-based aggregation algorithms for federated learning. We also present a scalable framework for practical federated learning. We empirically evaluate the performance by training deep convolutional neural networks on the MNIST dataset and the CIFAR10 dataset.
We present an algorithm to dynamically adjust the data assigned for each worker at every epoch during the training in a heterogeneous cluster. We empirically evaluate the performance of the dynamic partitioning by training deep neural networks on the CIFAR10 dataset.
We present a set of all-reduce compatible gradient compression algorithms which significantly reduce the communication overhead while maintaining the performance of vanilla SGD. We empirically evaluate the performance of the compression methods by training deep neural networks on the CIFAR10 dataset.
MFBN: Multilevel framework for bipartite networks
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