What are some resources and communities where practitioners can learn more about overfitting and share their experiences?

Oct 15, 2010 ... A highly overfitted decision boundary might perfectly classify the training data, even if the response patterns contain no information about the ... Over the last five decades, the percentages of New Economy and Old Economy Loss firms have significantly increased. However, most firms in the economy are Old ... Nov 13, 2024 ... Malicious aggregation servers or end-devices could potentially extract sensitive information from others' model updates, emphasizing the need ... Any risk of informational harm to data subjects and/or organization(s) ... overfitting the training data. Regulation. Rules or laws defined and enforced ... Nov 12, 2024 ... From our MACD data, we created graphs of the MACD indicator and the corresponding signal line over 26 day periods, which served as our image ... Mar 11, 2023 ... Overfitting or Underfitting the Training Data. Overfitting and underfitting are common problems in machine learning that occur when a model ... Nov 21, 2024 ... An Information-Theoretic Perspective on Overfitting and Underfitting ... We present an information-theoretic framework for understanding ... Dec 5, 2019 ... Market Efficiency in Theory and Reality. Informational efficiency in a market stems from competition for profits, low transaction costs, and ... To address this issue, prior research has adopted two approaches: contrastive learning and information maximization. Contrastive-learning [10, 19, 34, 35, 14, ... Sep 15, 2021 ... Critics noted that patients were not afforded agency over the use of their information, nor were privacy impacts adequately discussed [22]. A ...