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 3721–3730 of 4891 papers

TitleStatusHype
Bandit-Driven Batch Selection for Robust Learning under Label Noise—0
Basis functions nonlinear data-enabled predictive control: Consistent and computationally efficient formulations—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression—0
Batch-FPM: Random batch-update multi-parameter physical Fourier ptychography neural network—0
Batch Normalization Sampling—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Bayesian Estimation and Tuning-Free Rank Detection for Probability Mass Function Tensors—0
Bayesian inference for low rank spatiotemporal neural receptive fields—0
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified