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 731–740 of 4891 papers

TitleStatusHype
CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMsCode1
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
BetterNet: An Efficient CNN Architecture with Residual Learning and Attention for Precision Polyp SegmentationCode1
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive ReductionCode1
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud RegistrationCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
InRank: Incremental Low-Rank LearningCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
How Much Can Time-related Features Enhance Time Series Forecasting?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified