Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...
AI and machine learning are revolutionizing drug discovery, development, and lifecycle management, addressing industry ...
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Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics
MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...
Abhijeet Sudhakar develops efficient Mamba model training for machine learning, improving sequence modelling and ...
More than a decade ago, researchers launched the BabySeq Project, a pilot program to return newborn genomic sequencing results to parents and measure the effects on newborn care. Today, over 30 ...
eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...
Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...
A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.
In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.
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