Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

The remarkable practical success of deep learning has revealed some major surprises from
a theoretical perspective. In particular, simple gradient methods easily find near-optimal …

We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …

In the past decade the mathematical theory of machine learning has lagged far behind the
triumphs of deep neural networks on practical challenges. However, the gap between theory …

Attention mechanism is a central component of the transformer architecture which led to the
phenomenal success of large language models. However, the theoretical principles …

Breakthroughs in machine learning are rapidly changing science and society, yet our
fundamental understanding of this technology has lagged far behind. Indeed, one of the …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

摘要AdaBoost 是最优秀的Boosting 算法之一, 有着坚实的理论基础, 在实践中得到了很好的
推广和应用. 算法能够将比随机猜测略好的弱分类器提升为分类精度高的强分类器 …