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 1121–1130 of 4891 papers

TitleStatusHype
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learningCode0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Generative Archimedean CopulasCode0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
GNNMerge: Merging of GNN Models Without Accessing Training DataCode0
HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark DetectionCode0
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional SystemsCode0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified