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 1241–1250 of 4891 papers

TitleStatusHype
AttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing GraphsCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
GFSNetwork: Differentiable Feature Selection via Gumbel-Sigmoid RelaxationCode0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
AdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
Attention Gated Networks: Learning to Leverage Salient Regions in Medical ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified