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 3211–3220 of 4891 papers

TitleStatusHype
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees—0
A denoised Mean Teacher for domain adaptive point cloud registrationCode0
Verification of Neural Network Control Systems using Symbolic Zonotopes and Polynotopes—0
PathMLP: Smooth Path Towards High-order HomophilyCode0
Towards quantum enhanced adversarial robustness in machine learning—0
Neural Multigrid Memory For Computational Fluid DynamicsCode0
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems—0
Generalized Random Forests using Fixed-Point TreesCode0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference—0
Show:102550
← PrevPage 322 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified