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 461–470 of 4891 papers

TitleStatusHype
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions—0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks—0
Contract-based hierarchical control using predictive feasibility value functions—0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data—0
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image ClassificationCode0
Leave-One-Out Stable Conformal PredictionCode0
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models—0
Learning Transferable Friction Models and LuGre Identification via Physics Informed Neural Networks—0
Regist3R: Incremental Registration with Stereo Foundation Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified