PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning

Hyperparameters of Deep Learning (DL) pipelines are crucial for their downstream performance. While a large number of methods for Hyperparameter Optimization (HPO) have been developed, their incurred costs are often untenable for modern DL. Consequently, manual experimentation is still the most prevalent approach to optimize hyperparameters, relying on the researcher's intuition, domain knowledge, and cheap preliminary explorations. To resolve this misalignment between HPO algorithms and DL researchers, we propose PriorBand, an HPO algorithm tailored to DL, able to utilize both expert beliefs and cheap proxy tasks. Empirically, we demonstrate PriorBand's efficiency across a range of DL benchmarks and show its gains under informative expert input and robustness against poor expert belief

The level of knowledge about leprosy among university students [Nivel de conocimientos sobre la lepra en estudiantes universitarios.]

A survey about leprosy was made in 1,000 students from the University of Guadalajara (Guadalajara, Jalisco, Mexico). There were considered clinical, preventive, social and etiological aspects. The results showed that the patient suffering leprosy is currently marginated. We suggest that this study should be carried out in other universities of Mexico, with purposes to verify the stigmata of this entity

OpenML-Python: an extensible Python API for OpenML

OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper, we introduce OpenML-Python, a client API for Python, which opens up the OpenML platform for a wide range of Python-based machine learning tools. It provides easy access to all datasets, tasks and experiments on OpenML from within Python. It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML. Furthermore, it comes with a scikit-learn extension and an extension mechanism to easily integrate other machine learning libraries written in Python into the OpenML ecosystem. Source code and documentation are available at https://github.com/openml/openml-python/