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 1201–1210 of 4891 papers

TitleStatusHype
Regist3R: Incremental Registration with Stereo Foundation Model—0
Leave-One-Out Stable Conformal PredictionCode0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical featuresCode0
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions—0
Contract-based hierarchical control using predictive feasibility value functions—0
Fast-Powerformer: A Memory-Efficient Transformer for Accurate Mid-Term Wind Power Forecasting—0
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection—0
QAMA: Quantum annealing multi-head attention operator with classical deep learning framework—0
Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised ApproachCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified