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 591–600 of 4891 papers

TitleStatusHype
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia AlarmsCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP TasksCode1
Efficient and Effective Augmentation Strategy for Adversarial TrainingCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial VideosCode1
Mixture of Attention Heads: Selecting Attention Heads Per TokenCode1
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNCode1
Learning Signal Temporal Logic through Neural Network for Interpretable ClassificationCode1
Centroid Distance Keypoint Detector for Colored Point CloudsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified