11 citations · 30 across the 6 of their papers we have counts for
7 papers
Hierarchical Time Series Forecasting with Operator-Valued Kernels
Junjie Yang, Adeline Fermanian, Claire Boyer +1
Hierarchical time series forecasting requires generating accurate predictions that are coherent across multiple levels of aggregation. Existing methods either rely on two-stage pip…
The insertion method to invert the signature of a path
Adeline Fermanian, Jiawei Chang, Terry Lyons +1
The signature is a representation of a path as an infinite sequence of its iterated integrals. Under certain assumptions, the signature characterizes the path, up to translation an…
New directions in the applications of rough path theory
Adeline Fermanian, Terry Lyons, James Morrill +1
This article provides a concise overview of some of the recent advances in the application of rough path theory to machine learning. Controlled differential equations (CDEs) are di…
Scaling ResNets in the Large-depth Regime
Pierre Marion, Adeline Fermanian, Gérard Biau +1
Deep ResNets are recognized for achieving state-of-the-art results in complex machine learning tasks. However, the remarkable performance of these architectures relies on a trainin…
Framing RNN as a kernel method: A neural ODE approach
Adeline Fermanian, Pierre Marion, Jean-Philippe Vert +1
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…
A Generalised Signature Method for Multivariate Time Series Feature Extraction
James Morrill, Adeline Fermanian, Patrick Kidger +1
The 'signature method' refers to a collection of feature extraction techniques for multivariate time series, derived from the theory of controlled differential equations. There is…
Embedding and learning with signatures
Adeline Fermanian
Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. A novel approach for sequential learning, called the sign…