SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 1071–1080 of 4891 papers

TitleStatusHype
Binary Stereo MatchingCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS DemosaicingCode0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image ClassificationCode0
GNNMerge: Merging of GNN Models Without Accessing Training DataCode0
GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid RelaxationCode0
Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline ExtractionCode0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified