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 2111–2120 of 4891 papers

TitleStatusHype
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification—0
Automated Defect Detection and Grading of Piarom Dates Using Deep Learning—0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
Combinatorial Logistic BanditsCode0
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Linear Partial Gromov-Wasserstein EmbeddingCode0
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning—0
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models—0
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional ElementsCode0
Learning-to-Defer for Extractive Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified