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 251–260 of 4891 papers

TitleStatusHype
FAMO: Fast Adaptive Multitask OptimizationCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Birdie: Advancing State Space Models with Reward-Driven Objectives and CurriculaCode1
Event-based Video Reconstruction via Potential-assisted Spiking Neural NetworkCode1
BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp SegmentationCode1
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather PredictionCode1
Benchmarking the Robustness of Spatial-Temporal Models Against CorruptionsCode1
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive ReductionCode1
Electronic-structure properties from atom-centered predictions of the electron densityCode1
Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified