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 1251–1260 of 4891 papers

TitleStatusHype
Neural network-enhanced integrators for simulating ordinary differential equations—0
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
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand UncertaintyCode0
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model CompressionCode0
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
On the Connection Between Diffusion Models and Molecular Dynamics—0
DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework—0
Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified