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 2461–2470 of 4891 papers

TitleStatusHype
Faster Machine Unlearning via Natural Gradient Descent—0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control—0
Machine Unlearning for Medical Imaging—0
HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image ClassificationCode0
Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation—0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian—0
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads—0
A third-order finite difference weighted essentially non-oscillatory scheme with shallow neural network—0
PCAC-GAN: A Sparse-Tensor-Based Generative Adversarial Network for 3D Point Cloud Attribute Compression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified