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 701–710 of 4891 papers

TitleStatusHype
PATS: Process-Level Adaptive Thinking Mode SwitchingCode1
HiPart: Hierarchical Divisive Clustering ToolboxCode1
Hexatagging: Projective Dependency Parsing as TaggingCode1
Heat flux for semi-local machine-learning potentialsCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer ApproachCode1
Posterior Sampling for Deep Reinforcement LearningCode1
HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic WorkflowsCode1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified