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 3911–3920 of 4891 papers

TitleStatusHype
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks—0
Statistically Consistent Saliency Estimation—0
RDI-Net: Relational Dynamic Inference NetworksCode0
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space—0
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion—0
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Estimation of Number of Communities in Assortative Sparse Networks—0
Optimizing Over All Sequences of Orthogonal Polynomials—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified