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 4001–4010 of 4891 papers

TitleStatusHype
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization—0
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization—0
RDI-Net: Relational Dynamic Inference NetworksCode0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks—0
Optimizing Quantized Neural Networks with Natural Gradient—0
Self-Supervised Learning of Compressed Video Representations—0
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion—0
A Robust and Efficient Framework for Sports-Field Registration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified