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 4011–4020 of 4891 papers

TitleStatusHype
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation—0
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image ClassificationCode1
PP-OCR: A Practical Ultra Lightweight OCR SystemCode2
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based ModelsCode0
Overlapping community detection in networks via sparse spectral decompositionCode0
Improving Spiking Sparse Recovery via Non-Convex Penalties—0
Event-based update of synapses in voltage-based learning rules—0
Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method—0
Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERTCode1
A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified