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 781–790 of 4891 papers

TitleStatusHype
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image ClassificationCode1
Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISACCode1
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion InferenceCode1
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP TasksCode1
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
Adaptive wavelet distillation from neural networks through interpretationsCode1
FedPop: Federated Population-based Hyperparameter TuningCode1
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed AugmentationCode1
INSPIRE: Intensity and spatial information-based deformable image registrationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified