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 4111–4120 of 4891 papers

TitleStatusHype
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences—0
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to EndCode1
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach—0
Anomaly Detection with Tensor Networks—0
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models—0
On scenario construction for stochastic shortest path problems in real road networks—0
Learning to Generate 3D Training Data Through Hybrid Gradient—0
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationCode1
Instability, Computational Efficiency and Statistical Accuracy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified