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 3571–3580 of 4891 papers

TitleStatusHype
Stable Long-Term Recurrent Video Super-ResolutionCode1
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce—0
Efficient Geometry-aware 3D Generative Adversarial NetworksCode2
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection—0
TRACER: Extreme Attention Guided Salient Object Tracing NetworkCode1
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control—0
Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations—0
LSTM-based model predictive control with discrete inputs for irrigation scheduling—0
A Sparse Expansion For Deep Gaussian Processes—0
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic SurgeryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified