Integrating explanation and prediction in computational social science
Computational social science is more than just large repositories of digital data and the
computational methods needed to construct and analyse them. It also represents a …
computational methods needed to construct and analyse them. It also represents a …
Digital medicine and the curse of dimensionality
Digital health data are multimodal and high-dimensional. A patient's health state can be
characterized by a multitude of signals including medical imaging, clinical variables …
characterized by a multitude of signals including medical imaging, clinical variables …
In search of lost domain generalization
I Gulrajani, D Lopez-Paz - arXiv preprint arXiv:2007.01434, 2020 - arxiv.org
The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …
those seen during training. While a myriad of domain generalization algorithms exist …
Auto-sklearn 2.0: Hands-free automl via meta-learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …
tedious task of designing machine learning pipelines and has recently achieved substantial …
A deep analysis of brain tumor detection from mr images using deep learning networks
Creating machines that behave and work in a way similar to humans is the objective of
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving …
Causality matters in medical imaging
Causal reasoning can shed new light on the major challenges in machine learning for
medical imaging: scarcity of high-quality annotated data and mismatch between the …
medical imaging: scarcity of high-quality annotated data and mismatch between the …
[图书][B] Fairness and machine learning: Limitations and opportunities
An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …
fairness and machine learning. Fairness and Machine Learning introduces advanced …
Predicting perceived stress related to the Covid-19 outbreak through stable psychological traits and machine learning models
The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on
people's daily lives, with strong implications for stress levels due to the threat of contagion …
people's daily lives, with strong implications for stress levels due to the threat of contagion …
Adaptive machine unlearning
Data deletion algorithms aim to remove the influence of deleted data points from trained
models at a cheaper computational cost than fully retraining those models. However, for …
models at a cheaper computational cost than fully retraining those models. However, for …
Auto-pytorch: Multi-fidelity metalearning for efficient and robust autodl
While early AutoML frameworks focused on optimizing traditional ML pipelines and their
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …
hyperparameters, a recent trend in AutoML is to focus on neural architecture search. In this …