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 2031–2040 of 4891 papers

TitleStatusHype
Multilingual De-Duplication Strategies: Applying scalable similarity search with monolingual & multilingual embedding models—0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
VoCo-LLaMA: Towards Vision Compression with Large Language ModelsCode3
Explainable Bayesian Recurrent Neural Smoother to Capture Global State Evolutionary Correlations—0
Meta Reasoning for Large Language Models—0
MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic—0
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation ModelCode3
YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention MechanismCode1
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and ForecastingCode2
Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A SurveyCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified