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 3721–3730 of 4891 papers

TitleStatusHype
MISO hierarchical inference engine satisfying the law of importation with aggregation functions—0
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach—0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution NetworkCode0
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce—0
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds—0
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified