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 431–440 of 4891 papers

TitleStatusHype
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint PredictionCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-ResolutionCode1
InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck MambaCode1
InRank: Incremental Low-Rank LearningCode1
INSPIRE: Intensity and spatial information-based deformable image registrationCode1
InterFormer: Real-time Interactive Image SegmentationCode1
DiGRAF: Diffeomorphic Graph-Adaptive Activation FunctionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified