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 4091–4100 of 4891 papers

TitleStatusHype
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems—0
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation—0
Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering—0
Adversarial Imitation Learning via Random Search—0
Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables—0
Intelligence plays dice: Stochasticity is essential for machine learning—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting—0
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic HierarchyCode0
A Survey on Large-scale Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified