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 2651–2660 of 4891 papers

TitleStatusHype
Multi-view learning for automatic classification of multi-wavelength auroral images—0
Riemannian Laplace Approximation with the Fisher MetricCode0
Mixed Models with Multiple Instance LearningCode1
Successor Features for Efficient Multisubject Controlled Text Generation—0
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine—0
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders—0
Learning Collective Behaviors from Observation—0
Electronic excited states from physically-constrained machine learning—0
Bandit-Driven Batch Selection for Robust Learning under Label Noise—0
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified