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 1121–1130 of 4891 papers

TitleStatusHype
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment—0
Dendritic Computing with Multi-Gate Ferroelectric Field-Effect Transistors—0
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging—0
Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning—0
GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation—0
Multi-start Optimization Method via Scalarization based on Target Point-based Tchebycheff Distance for Multi-objective Optimization—0
Safety-Critical Traffic Simulation with Guided Latent Diffusion Model—0
Do global forecasting models require frequent retraining?—0
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX—0
Gaussian Process Policy Iteration with Additive Schwarz Acceleration for Forward and Inverse HJB and Mean Field Game Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified