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 2971–2980 of 4891 papers

TitleStatusHype
Identification of stormwater control strategies and their associated uncertainties using Bayesian OptimizationCode0
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-ConcatenationCode0
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness—0
Bi-fidelity Variational Auto-encoder for Uncertainty QuantificationCode0
Neural incomplete factorization: learning preconditioners for the conjugate gradient methodCode1
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement LearningCode1
Leveraging object detection for the identification of lung cancer—0
Union Subgraph Neural NetworksCode0
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)Code1
Deep Reinforcement Learning with Plasticity Injection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified