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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4031–4040 of 4891 papers

TitleStatusHype
Deep Active Contours for Real-time 6-DoF Object Tracking—0
Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning—0
Deep-and-Wide Learning: Enhancing Data-Driven Inference via Synergistic Learning of Inter- and Intra-Data Representations—0
Deep Auto-Deferring Policy for Combinatorial Optimization—0
Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce—0
Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications—0
Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing—0
DeepF-fNet: a physics-informed neural network for vibration isolation optimization—0
Deep FisherNet for Object Classification—0
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified