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 3931–3940 of 4891 papers

TitleStatusHype
Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction—0
Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Characterizing the Evasion Attackability of Multi-label Classifiers—0
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering—0
Optimal transport for vector Gaussian mixture models—0
Scalable Verification of Quantized Neural Networks (Technical Report)Code0
INSPIRE: Intensity and spatial information-based deformable image registrationCode1
Monocular Real-time Full Body Capture with Inter-part Correlations—0
An empirical evaluation of functional alignment using inter-subject decodingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified