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 791–800 of 4891 papers

TitleStatusHype
Simple random search provides a competitive approach to reinforcement learningCode1
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution LayersCode1
featsel: A framework for benchmarking of feature selection algorithms and cost functionsCode1
Fast and Accurate Entity Recognition with Iterated Dilated ConvolutionsCode1
Rethinking the Inception Architecture for Computer VisionCode1
Towards Good Practices for Very Deep Two-Stream ConvNetsCode1
Sparse Projection Oblique Randomer ForestsCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services—0
Computational-Statistical Tradeoffs from NP-hardness—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified