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 3201–3210 of 4891 papers

TitleStatusHype
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression—0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction—0
SUGAR: Spherical Ultrafast Graph Attention Framework for Cortical Surface Registration—0
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations—0
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the SpectrumCode0
Weighted Anisotropic-Isotropic Total Variation for Poisson DenoisingCode0
Scalable method for Bayesian experimental design without integrating over posterior distributionCode0
Large-scale Bayesian Structure Learning for Gaussian Graphical Models using Marginal Pseudo-likelihoodCode0
What Truly Matters in Trajectory Prediction for Autonomous Driving?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified