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 1331–1340 of 4891 papers

TitleStatusHype
Gracefully Filtering Backdoor Samples for Generative Large Language Models without RetrainingCode1
Real-time Traffic Simulation and Management for Large-scale Urban Air Mobility: Integrating Route Guidance and Collision Avoidance—0
Iterative Distributed Multinomial RegressionCode0
RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model AccuracyCode0
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data—0
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking—0
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging DatasetsCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning—0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified