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

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Showing 1901–1910 of 4891 papers

TitleStatusHype
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Fairness-aware Classification: Criterion, Convexity, and Bounds—0
CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models—0
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels—0
Feature-Specific Coefficients of Determination in Tree Ensembles—0
Feature subset selection for kernel SVM classification via mixed-integer optimization—0
Factorized Asymptotic Bayesian Inference for Latent Feature Models—0
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain—0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data—0
CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified