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 3481–3490 of 4891 papers

TitleStatusHype
The mass-conversion method: a hybrid technique for simulating well-mixed chemical reaction networks—0
Joint Speech Translation and Named Entity Recognition—0
Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance—0
gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs—0
Graph sampling for node embedding—0
Phenomenological Model of Superconducting Optoelectronic Loop Neurons—0
RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline ExtractionCode0
Accelerate Three-Dimensional Generative Adversarial Networks Using Fast Algorithm—0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
Dynamics-aware Adversarial Attack of Adaptive Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified