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 3991–4000 of 4891 papers

TitleStatusHype
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
Resolution-Based Distillation for Efficient Histology Image Classification—0
A novel shape matching descriptor for real-time hand gesture recognition—0
Optimizing Over All Sequences of Orthogonal Polynomials—0
Statistically Consistent Saliency Estimation—0
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation—0
Balancing training time vs. performance with Bayesian Early Pruning—0
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Estimation of Number of Communities in Assortative Sparse Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified