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 1711–1720 of 4891 papers

TitleStatusHype
Which price to pay? Auto-tuning building MPC controller for optimal economic cost—0
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning—0
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems—0
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data—0
DPERC: Direct Parameter Estimation for Mixed Data—0
Deep Learning for Early Alzheimer Disease Detection with MRI ScansCode0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—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
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified