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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1231–1240 of 4891 papers

TitleStatusHype
GCSAM: Gradient Centralized Sharpness Aware MinimizationCode0
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
Demystifying the Effect of Receptive Field Size in U-Net Models for Medical Image SegmentationCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian OptimizationCode0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
AdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified