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 1361–1370 of 4891 papers

TitleStatusHype
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification—0
Enhancing Character-Level Understanding in LLMs through Token Internal Structure LearningCode0
On the Efficiency of NLP-Inspired Methods for Tabular Deep LearningCode3
SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates—0
GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation—0
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation—0
Perceptually Optimized Super Resolution—0
Spatio-temporal Causal Learning for Streamflow Forecasting—0
Star Attention: Efficient LLM Inference over Long SequencesCode3
Efficient Deployment of Transformer Models in Analog In-Memory Computing HardwareCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified