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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3901–3910 of 4891 papers

TitleStatusHype
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning—0
Data-driven sparse polynomial chaos expansion for models with dependent inputs—0
ZeRO-Offload: Democratizing Billion-Scale Model TrainingCode0
Frequency-weighted H2-optimal model order reduction via oblique projection—0
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage KernelsCode1
Resolution-Based Distillation for Efficient Histology Image Classification—0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
A novel shape matching descriptor for real-time hand gesture recognition—0
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive ReductionCode1
A Robust and Efficient Framework for Sports-Field Registration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified