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 2431–2440 of 4891 papers

TitleStatusHype
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model—0
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems—0
Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique—0
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models—0
EDGE++: Improved Training and Sampling of EDGE—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Analytically Tractable Inference in Deep Neural Networks—0
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified