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 1521–1530 of 4891 papers

TitleStatusHype
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control—0
Probabilistic Formulations for System Identification of Linear Dynamics with Bilinear Observation Models—0
Hard constraint learning approaches with trainable influence functions for evolutionary equationsCode0
QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification—0
An Enhancement of Jiang, Z., et al.s Compression-Based Classification Algorithm Applied to News Article Categorization—0
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly—0
Financial fraud detection system based on improved random forest and gradient boosting machine (GBM)—0
Fundamental Survey on Neuromorphic Based Audio Classification—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
YOLOv12: A Breakdown of the Key Architectural Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified