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 3291–3300 of 4891 papers

TitleStatusHype
Centroid Distance Keypoint Detector for Colored Point CloudsCode1
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
Deep Learning-based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement—0
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation—0
Merging Classification Predictions with Sequential Information for Lightweight Visual Place Recognition in Changing Environments—0
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network—0
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis—0
RotateCT: Knowledge Graph Embedding by Rotation and Coordinate Transformation in Complex Space—0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph—0
Learning Hierarchical Image Segmentation For Recognition and By RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified