Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.

Unfortunately, my initial hands-on testing with corrupted datasets reveals a fundamental enterprise problem: impressive capabilities paired with insufficient transparency about data transformations.

This paper proposes a time-series process event log preprocessing approach applied to realize data-intensive and predictive operationalization of IoT-supported smart factories. The advanced conceptual ...

This study offers important insights into the development of infants' responses to music based on the exploration of EEG neural auditory responses and video-based movement analysis. The convincing ...

Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks.New libraries like JAX, ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

Industrial time series, as a kind of data that responds to production process information, can be analyzed and predicted for effective monitoring of industrial production processes. There are problems ...

aws data-science machine-learning timeseries deep-learning time-series mxnet torch pytorch artificial-intelligence neural-networks forecasting time-series-prediction time-series-forecasting sagemaker ...