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 3241–3250 of 4891 papers

TitleStatusHype
Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks—0
Exploring the effects of robotic design on learning and neural controlCode0
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks—0
The Power Of Simplicity: Why Simple Linear Models Outperform Complex Machine Learning Techniques -- Case Of Breast Cancer Diagnosis—0
Overcoming the Stability Gap in Continual Learning—0
Do we become wiser with time? On causal equivalence with tiered background knowledge—0
Interaction Measures, Partition Lattices and Kernel Tests for High-Order InteractionsCode0
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models—0
Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria RecommendationCode0
Identification of stormwater control strategies and their associated uncertainties using Bayesian OptimizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified