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 2311–2320 of 4891 papers

TitleStatusHype
Enhancing Vision-Language Few-Shot Adaptation with Negative LearningCode1
XPose: eXplainable Human Pose Estimation—0
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness—0
Neural Parameter Regression for Explicit Representations of PDE Solution Operators—0
A Clustering Method with Graph Maximum Decoding Information—0
Learning Dynamical Systems Encoding Non-Linearity within Space CurvatureCode0
Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional Computing on Embedded Devices—0
The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection—0
Improving LoRA in Privacy-preserving Federated Learning—0
Variational Approach for Efficient KL Divergence Estimation in Dirichlet Mixture Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified