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 61–70 of 4891 papers

TitleStatusHype
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior—0
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation—0
Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images—0
Islanding Strategy for Smart Grids Oriented to Resilience Enhancement and Its Power Supply Range Optimization—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference—0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems—0
sHGCN: Simplified hyperbolic graph convolutional neural networksCode0
Accurate and scalable exchange-correlation with deep learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified