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 451–460 of 4891 papers

TitleStatusHype
Anchor-based Large Language ModelsCode1
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
Representation Surgery for Multi-Task Model MergingCode1
Scalable Multi-modal Model Predictive Control via Duality-based Interaction PredictionsCode1
Multivariate Probabilistic Time Series Forecasting with Correlated ErrorsCode1
Speeding up and reducing memory usage for scientific machine learning via mixed precisionCode1
Open-RadVLAD: Fast and Robust Radar Place RecognitionCode1
Learning to Manipulate Artistic ImagesCode1
Unified-Width Adaptive Dynamic Network for All-In-One Image RestorationCode1
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-AttentionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified