Residual neural networks are state-of-the-art deep learning models. Thei...
Physics-informed neural networks (PINNs) are a promising approach that
c...
The signature is a representation of a path as an infinite sequence of i...
Deep ResNets are recognized for achieving state-of-the-art results in co...
The mathematical forces at work behind Generative Adversarial Networks r...
Building on the interpretation of a recurrent neural network (RNN) as a
...
Interpretability of learning algorithms is crucial for applications invo...
Recent advances in adversarial attacks and Wasserstein GANs have advocat...
We present new insights into causal inference in the context of Heteroge...
Generative Adversarial Networks (GANs) have been successful in producing...
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a...
State-of-the-art learning algorithms, such as random forests or neural
n...
Gradient tree boosting is a prediction algorithm that sequentially produ...
Given an ensemble of randomized regression trees, it is possible to
rest...
The random forest algorithm, proposed by L. Breiman in 2001, has been
ex...
Distributed computing offers a high degree of flexibility to accommodate...
Random forests are a learning algorithm proposed by Breiman [Mach. Learn...
The cellular tree classifier model addresses a fundamental problem in th...
Collaborative recommendation is an information-filtering technique that
...