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 3411–3420 of 4891 papers

TitleStatusHype
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd CountingCode0
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models—0
Learning Ego 3D Representation as Ray TracingCode1
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training—0
8-bit Numerical Formats for Deep Neural Networks—0
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow—0
A Survey on Computationally Efficient Neural Architecture Search—0
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
Searching for COMETINHO: The Little Metric That Could—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified