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 4171–4180 of 4891 papers

TitleStatusHype
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Do global forecasting models require frequent retraining?—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems—0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction—0
Domain-Specific Japanese ELECTRA Model Using a Small Corpus—0
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX—0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Show:102550
← PrevPage 418 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified