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 1171–1180 of 4891 papers

TitleStatusHype
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
Autonomous Sparse Mean-CVaR Portfolio OptimizationCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional SystemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified