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 2601–2610 of 4891 papers

TitleStatusHype
Spatially Covariant Image Registration with Text PromptsCode1
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs—0
Automated Lane Merging via Game Theory and Branch Model Predictive ControlCode1
Online Importance Sampling for Stochastic Gradient Optimization—0
CRISP: Hybrid Structured Sparsity for Class-aware Model PruningCode0
Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive ImagingCode1
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores—0
A projected nonlinear state-space model for forecasting time series signalsCode0
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints—0
Show:102550
← PrevPage 261 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified