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 2991–3000 of 4891 papers

TitleStatusHype
SigScatNet: A Siamese + Scattering based Deep Learning Approach for Signature Forgery Detection and Similarity Assessment—0
Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization—0
Perfecting Liquid-State Theories with Machine Intelligence—0
Basis functions nonlinear data-enabled predictive control: Consistent and computationally efficient formulations—0
High-Performance Hybrid Algorithm for Minimum Sum-of-Squares Clustering of Infinitely Tall DataCode0
Extending Machine Learning-Based Early Sepsis Detection to Different Demographics—0
Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character DecompositionCode0
Beyond Traditional Beamforming: Singular Vector Projection for MU-MIMO—0
Multi-view learning for automatic classification of multi-wavelength auroral images—0
Leveraging point annotations in segmentation learning with boundary loss—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified