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 1481–1490 of 4891 papers

TitleStatusHype
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Efficient and Accurate Full-Waveform Inversion with Total Variation ConstraintCode0
FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPICode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
A Spatially-Aware Multiple Instance Learning Framework for Digital PathologyCode0
Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained ModelsCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified