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 811–820 of 4891 papers

TitleStatusHype
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming—0
Fast Gaussian Processes under Monotonicity Constraints—0
Critical Nodes Identification in Complex Networks: A Survey—0
Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification—0
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models—0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
A Survey on Prompt TuningCode0
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study—0
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction—0
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified