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 3401–3410 of 4891 papers

TitleStatusHype
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning—0
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network—0
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic ModelsCode1
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions—0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackCode0
SpecNet2: Orthogonalization-free spectral embedding by neural networksCode0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Stochastic Gradient Descent without Full Data ShuffleCode0
Geometric Policy Iteration for Markov Decision Processes—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified