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 1461–1470 of 4891 papers

TitleStatusHype
Data-to-Model Distillation: Data-Efficient Learning FrameworkCode0
Action Recognition in Real-World Ambient Assisted Living EnvironmentCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record GenerationCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissueCode0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPICode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified