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 3381–3390 of 4891 papers

TitleStatusHype
A Novel Noise Injection-based Training Scheme for Better Model Robustness—0
Creating generalizable downstream graph models with random projections—0
A model-free feature selection technique of feature screening and random forest based recursive feature elimination—0
Fast and Blind Speech Copy-Move Detection and Localization in Noise—0
Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model—0
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution ShiftsCode0
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and DeploymentCode0
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models—0
Learning to Select Pivotal Samples for Meta Re-weightingCode0
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified