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 1181–1190 of 4891 papers

TitleStatusHype
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian OptimizationCode0
Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover MappingCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration StrategyCode0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion ModelsCode0
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer EvaluationsCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified