Abstract: Machine learning systems often require updates for various reasons, such as the availability of new data or models and the need to optimize different technical or ethical metrics. Typically, ...
The framework predicts how proteins will function with several interacting mutations and finds combinations that work well together.
How we learn to predict an outcome isn’t determined by how many times a cue and reward happen together. Instead, how much ...
New forms of fentanyl are created every day. For law enforcement, that poses a challenge: How do you identify a chemical you've never seen before? Researchers at Lawrence Livermore National Laboratory ...
A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
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Financial word of the day: Heteroscedasticity — meaning, usage, and why it matters more than ever
Heteroscedasticity describes a situation where risk (variance) changes with the level of a variable. In financial models, this means volatility is not constant. Most pricing and forecasting models ...
Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...
The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
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