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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1431–1440 of 4891 papers

TitleStatusHype
A Triple-Inertial Accelerated Alternating Optimization Method for Deep Learning TrainingCode0
MC-GRU:a Multi-Channel GRU network for generalized nonlinear structural response prediction across structures—0
Skelite: Compact Neural Networks for Efficient Iterative SkeletonizationCode0
How Well Can Differential Privacy Be Audited in One Run?—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
Reinforcement Learning Based Symbolic Regression for Load Modeling—0
Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar—0
Discrete Gaussian Process Representations for Optimising UAV-based Precision Weed Mapping—0
A Unified View of Optimal Kernel Hypothesis Testing—0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified