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

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Showing 2031–2040 of 4891 papers

TitleStatusHype
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Universal on-chip polarization handling with deep photonic networks—0
Graph Neural Networks for modelling breast biomechanical compressionCode0
Local vs. Global Models for Hierarchical Forecasting—0
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes—0
Locally Adaptive One-Class Classifier Fusion with Dynamic p-Norm Constraints for Robust Anomaly Detection—0
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data—0
A Survey on Kolmogorov-Arnold Network—0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning—0
Physics-constrained coupled neural differential equations for one dimensional blood flow modelingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified