IEEE

Deep Learning and Symbolic Regression for Discovering Parametric Equations

Abstract: Symbolic regression is a machine learning technique that can learn the equations governing data and thus has the potential to transform scientific discovery. However, symbolic regression is ...
IEEE

Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

Abstract: Vibration signals under the same health state often have large differences due to changes in operating conditions. Likewise, the differences among vibration signals under different health ...
Los Angeles Times

These are L.A.’s worst intersections. Why some might surprise you

Using traffic volume and accident data, The Times ranked the worst Los Angeles intersections. Not all of the most problematic intersections were on major thoroughfares. Everyone who commutes in L.A.
Seeking Alpha

PTC Therapeutics: Sephience As A Major Growth Driver

PTC Therapeutics is transitioning its growth engine from legacy drugs to new launches like Sephience, targeting rare genetic diseases with high unmet need. Pipeline catalysts, notably Votoplam for ...
GitHub

A PyTorch implementation of Parametric UMAP (Uniform Manifold Approximation and Projection) for learning low-dimensional parametric embeddings of high-dimensional data.

The pip install pulls the default PyTorch build from PyPI. If you need a specific CUDA version, install PyTorch first following the official instructions, then install this package.