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 4151–4160 of 4891 papers

TitleStatusHype
Combining the band-limited parameterization and Semi-Lagrangian Runge--Kutta integration for efficient PDE-constrained LDDMM—0
GAP++: Learning to generate target-conditioned adversarial examples—0
Neural Network Activation Quantization with Bitwise Information BottlenecksCode0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences—0
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
Learning to Generate 3D Training Data Through Hybrid Gradient—0
On scenario construction for stochastic shortest path problems in real road networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified