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 1441–1450 of 4891 papers

TitleStatusHype
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Adaptive Conditional Expert Selection Network for Multi-domain Recommendation—0
Large-scale moral machine experiment on large language modelsCode1
Shallow Signed Distance Functions for Kinematic Collision Bodies—0
Universal on-chip polarization handling with deep photonic networks—0
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection—0
Local vs. Global Models for Hierarchical Forecasting—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Graph Neural Networks for modelling breast biomechanical compressionCode0
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data—0
Show:102550
← PrevPage 145 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified