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 2481–2490 of 4891 papers

TitleStatusHype
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank—0
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning—0
RAPID: Robust APT Detection and Investigation Using Context-Aware Deep Learning—0
ReActXGB: A Hybrid Binary Convolutional Neural Network Architecture for Improved Performance and Computational Efficiency—0
Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach—0
Real-time 2D/3D Registration via CNN Regression—0
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models—0
Real-time decision-making for autonomous vehicles under faults—0
Dispatchable Region for Active Distribution Networks Using Approximate Second-Order Cone Relaxation—0
Real-Time Energy-Optimal Path Planning for Electric Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified