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 1071–1080 of 4891 papers

TitleStatusHype
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems—0
DPERC: Direct Parameter Estimation for Mixed Data—0
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning—0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
Deep Learning for Early Alzheimer Disease Detection with MRI ScansCode0
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data—0
Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards—0
Parallel multi-objective metaheuristics for smart communications in vehicular networks—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified