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 4041–4050 of 4891 papers

TitleStatusHype
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Intelligence plays dice: Stochasticity is essential for machine learning—0
Revisiting Temporal Modeling for Video Super-resolutionCode1
DensE: An Enhanced Non-commutative Representation for Knowledge Graph Embedding with Adaptive Semantic HierarchyCode0
A Survey on Large-scale Machine LearningCode0
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks—0
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance ProblemsCode1
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and HypergraphsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified