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 3541–3550 of 4891 papers

TitleStatusHype
Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature SharingCode0
Learning-based estimation of in-situ wind speed from underwater acoustics—0
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
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks—0
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
Robust Graph Neural Networks using Weighted Graph LaplacianCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified