arXiv:1909.06674
공식 코드 · Papers with Code
A Step Toward Quantifying Independently Reproducible Machine Learning Research
원문 초록
What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results. Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually attempting to implement 255 papers published from 1984 until 2017, recording features of each paper, and performing statistical analysis of the results. For each paper, we did not look at the authors code, if released, in order to prevent bias toward discrepancies between code and paper.
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AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks
Evolutionary bagging for ensemble learning
Neural network accelerator for quantum control
Deep Reinforcement Learning for Multi-Agent Interaction
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19
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