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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 671–680 of 4891 papers

TitleStatusHype
Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detection—0
Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text ClassificationCode0
SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments—0
Machine learning surrogate models of many-body dispersion interactions in polymer melts—0
Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network—0
Variational Autoencoded Multivariate Spatial Fay-Herriot Models—0
Involution and BSConv Multi-Depth Distillation Network for Lightweight Image Super-Resolution—0
Advances in 4D Generation: A SurveyCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified