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Found 17 papers, 8 papers with code
Physics-informed machine learning as a kernel method
In this context, we consider a general regression problem where the empirical risk is regularized by a partial differential equation that quantifies the physical inconsistency.
Implicit regularization of deep residual networks towards neural ODEs
Our results are valid for a finite training time, and also as the training time tends to infinity provided that the network satisfies a Polyak-Lojasiewicz condition.
Scaling ResNets in the Large-depth Regime
initializations, the only non-trivial dynamics is for $\alpha_L = 1/\sqrt{L}$ (other choices lead either to explosion or to identity mapping).
Optimal 1-Wasserstein Distance for WGANs
The mathematical forces at work behind Generative Adversarial Networks raise challenging theoretical issues.
Framing RNN as a kernel method: A neural ODE approach
Building on the interpretation of a recurrent neural network (RNN) as a continuous-time neural differential equation, we show, under appropriate conditions, that the solution of a RNN can be viewed as a linear function of a specific feature set of the input sequence, known as the signature.
SHAFF: Fast and consistent SHApley eFfect estimates via random Forests
Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable importance is one of the main interpretation tools.
Wasserstein Random Forests and Applications in Heterogeneous Treatment Effects
We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional distributions.
Some Theoretical Insights into Wasserstein GANs
Generative Adversarial Networks (GANs) have been successful in producing outstanding results in areas as diverse as image, video, and text generation.
Interpretable Random Forests via Rule Extraction
We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule learning algorithm which takes the form of a short and simple list of rules.
SIRUS: Stable and Interpretable RUle Set for Classification
State-of-the-art learning algorithms, such as random forests or neural networks, are often qualified as "black-boxes" because of the high number and complexity of operations involved in their prediction mechanism.
Accelerated Gradient Boosting
Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem.
Optimization by gradient boosting
Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem.
Neural Random Forests
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights.
A Random Forest Guided Tour
The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method.
Online Asynchronous Distributed Regression
Distributed computing offers a high degree of flexibility to accommodate modern learning constraints and the ever increasing size of datasets involved in massive data issues.
Consistency of random forests
What has greatly contributed to the popularity of forests is the fact that they can be applied to a wide range of prediction problems and have few parameters to tune.
Cellular Tree Classifiers
The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classification, or shall one split the data into two or more parts and send each part to a potentially different computer (or cell) for further processing?
