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 1291–1300 of 4891 papers

TitleStatusHype
Benchmarking Federated Machine Unlearning methods for Tabular Data—0
Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing—0
Efficient n-body simulations using physics informed graph neural networks—0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization—0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
On the Steady-State Distributionally Robust Kalman FilterCode0
CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching—0
Tree-Guided L_1-Convex ClusteringCode0
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables—0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
Show:102550
← PrevPage 130 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified