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 2811–2820 of 4891 papers

TitleStatusHype
Network Inversion of Binarised Neural Nets—0
Emulating the interstellar medium chemistry with neural operators—0
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications—0
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models—0
Turn Waste into Worth: Rectifying Top-k Router of MoE—0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
Collaborative Learning with Different Labeling Functions—0
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning—0
Private PAC Learning May be Harder than Online Learning—0
Closed-form Filtering for Non-linear Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified