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 2241–2250 of 4891 papers

TitleStatusHype
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks—0
Efficient Training of Very Deep Neural Networks for Supervised Hashing—0
Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning—0
An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention—0
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm—0
Efficient training of physics-informed neural networks via importance sampling—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified