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 2671–2680 of 4891 papers

TitleStatusHype
Scalable Machine Learning Algorithms using Path Signatures—0
Scalable Non-linear Learning with Adaptive Polynomial Expansions—0
Scalable Nonlinear Learning with Adaptive Polynomial Expansions—0
Scalable Smartphone Cluster for Deep Learning—0
Scalable Subsampling Inference for Deep Neural Networks—0
Scalable Vehicle Re-Identification via Self-Supervision—0
Scale-free Unconstrained Online Learning for Curved Losses—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Scaling Continuous Kernels with Sparse Fourier Domain Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified