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 3341–3350 of 4891 papers

TitleStatusHype
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networksCode0
ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set—0
Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram—0
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers—0
Automating DBSCAN via Deep Reinforcement LearningCode1
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks—0
Simplified State Space Layers for Sequence ModelingCode2
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework—0
Are Gradients on Graph Structure Reliable in Gray-box Attacks?Code0
On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical SolverCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified