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 4421–4430 of 4891 papers

TitleStatusHype
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationCode0
Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber—0
Accurate Hand Keypoint Localization on Mobile Devices—0
Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN—0
Shortcut Matrix Product States and its applications—0
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Learning Item-Interaction Embeddings for User Recommendations—0
Non-Intrusive Load Monitoring with Fully Convolutional Networks—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified