Business users can now determine the best course of action under real-world constraints and uncertainty, with input ...

CORAL (COnsistent RAnk Logits) and CORN (Conditional Ordinal Regression for Neural networks) are methods for ordinal regression with deep neural networks, which address the rank inconsistency issue of ...

WaveletML is an open-source Python framework designed for building, training, and evaluating Wavelet Neural Networks (WNNs) tailored for supervised learning tasks such as regression and classification ...

We explore the use of Physics-Informed Neural Networks (PINNs) for reconstructing full magnetohydrodynamic solutions from partial samples, mimicking the recreation of space-time environments around ...

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple ...

Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, “learn” ...

Matrix completion is a fundamental problem in machine learning that arises in various applications. We envision that our infinite width neural network framework for matrix completion will be easily ...