Search Results for author:
Found 5 papers, 4 papers with code
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks
Existing distributed systems load the entire graph in memory for graph partitioning, requiring a huge memory space to process large graphs and thus hindering GNN training on such large graphs using commodity workstations.
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal samples are available on each floor.
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks
To achieve faster networks, we revisit popular operators and demonstrate that such low FLOPS is mainly due to frequent memory access of the operators, especially the depthwise convolution.
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs.
Beyond Random Walk and Metropolis-Hastings Samplers: Why You Should Not Backtrack for Unbiased Graph Sampling
In this paper, we propose non-backtracking random walk with re-weighting (NBRW-rw) and MH algorithm with delayed acceptance (MHDA) which are theoretically guaranteed to achieve, at almost no additional cost, not only unbiased graph sampling but also higher efficiency (smaller asymptotic variance of the resulting unbiased estimators) than the SRW-rw and the MH algorithm, respectively.
Methodology Data Structures and Algorithms Networking and Internet Architecture Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
