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 3551–3560 of 4891 papers

TitleStatusHype
TSA-Net: Tube Self-Attention Network for Action Quality AssessmentCode1
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement—0
Jointly Efficient and Optimal Algorithms for Logistic BanditsCode0
S-Walk: Accurate and Scalable Session-based Recommendationwith Random WalksCode1
Learnable Lookup Table for Neural Network QuantizationCode1
Quality-aware Part Models for Occluded Person Re-identification—0
Electric Field Models of Transcranial Magnetic Stimulation Coils with Arbitrary Geometries: Reconstruction from Incomplete Magnetic Field MeasurementsCode0
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
MSeg: A Composite Dataset for Multi-domain Semantic SegmentationCode1
PRIME: A few primitives can boost robustness to common corruptionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified