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 2531–2540 of 4891 papers

TitleStatusHype
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization—0
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks—0
Analyzing Large Language Models for Classroom Discussion AssessmentCode0
StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream LearningCode0
Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models—0
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition—0
Data-driven Power Flow Linearization: Simulation—0
Economic Model Predictive Control of Water Distribution Systems with Accelerated Optimization Algorithm—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified