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 741–750 of 4891 papers

TitleStatusHype
EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer—0
MC-GRU:a Multi-Channel GRU network for generalized nonlinear structural response prediction across structures—0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
ActiveInitSplat: How Active Image Selection Helps Gaussian Splatting—0
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language ModelsCode1
Reinforcement Learning Based Symbolic Regression for Load Modeling—0
Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar—0
Skelite: Compact Neural Networks for Efficient Iterative SkeletonizationCode0
A Unified View of Optimal Kernel Hypothesis Testing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified