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 3581–3590 of 4891 papers

TitleStatusHype
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution—0
Fast Point TransformerCode1
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Fully Attentional Network for Semantic SegmentationCode1
SHRIMP: Sparser Random Feature Models via Iterative Magnitude PruningCode0
Self-Organized Polynomial-Time Coordination GraphsCode0
Universalizing Weak Supervision—0
Self-Supervised Camera Self-Calibration from Video—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified