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 1301–1310 of 4891 papers

TitleStatusHype
Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization—0
A Low-complexity Structured Neural Network to Realize States of Dynamical SystemsCode0
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip StatisticsCode0
Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation—0
Optimization of Layer Skipping and Frequency Scaling for Convolutional Neural Networks under Latency Constraint—0
Free Parametrization of L2-bounded State Space ModelsCode0
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization—0
Exact Characterization of Aggregate Flexibility via Generalized Polymatroids—0
A Time Splitting Based Optimization Method for Nonlinear MHE—0
Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified