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 3731–3740 of 4891 papers

TitleStatusHype
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic SurgeryCode0
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution—0
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
Self-Organized Polynomial-Time Coordination GraphsCode0
Universalizing Weak Supervision—0
SHRIMP: Sparser Random Feature Models via Iterative Magnitude PruningCode0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Is Class-Incremental Enough for Continual Learning?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified