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 531–540 of 4891 papers

TitleStatusHype
Confidence-Aware Learning Optimal Terminal Guidance via Gaussian Process Regression—0
Planning Safety Trajectories with Dual-Phase, Physics-Informed, and Transportation Knowledge-Driven Large Language ModelsCode0
Loss Functions in Deep Learning: A Comprehensive Review—0
Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration—0
Hybrid Real- and Complex-valued Neural Network Architecture—0
Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics—0
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex ScenariosCode0
On the Connection Between Diffusion Models and Molecular Dynamics—0
DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework—0
REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified