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 3341–3350 of 4891 papers

TitleStatusHype
Acoustic Source Localization with the Angular Spectrum Approach in Continuously Stratified Media—0
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
Action Recognition With Motion Diversification and Dynamic Selection—0
Action-Sufficient State Representation Learning for Control with Structural Constraints—0
ActiveInitSplat: How Active Image Selection Helps Gaussian Splatting—0
Active Learning in Video Tracking—0
Active Learning with Tabular Language Models—0
Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems—0
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
Ada-K Routing: Boosting the Efficiency of MoE-based LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified