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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.
A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations.
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.
TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing
Extensive experiments on face recognition show that TVConv reduces the computational cost by up to 3. 1x and improves the corresponding throughput by 2. 3x while maintaining a high accuracy compared to the depthwise convolution.
