10 papers
Advances in Neural Controlled Differential Equations
Benjamin Walker
Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences. Continuous-time approaches instead treat time series as s…
Universal Time Series Generation with Neural Controlled Differential Equations
Torben Berndt, Elyes Farjallah, Leif Seute +3
Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time-series modelling. While the…
Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs
Benjamin Walker, Alexandre Bloch, Lingyi Yang +2
Continuous-time models are a natural choice for irregular and asynchronous data. A central design choice is how to embed discrete observations into continuous time. Interpolation-…
Chess-World-Model: A 10M-Game Benchmark for Exact State Tracking from Chess Move Sequences
Benjamin Walker, Terry Lyons
World models require state tracking, which is the ability to maintain a correct latent state across action sequences. Existing benchmarks are often synthetic or language-based, lim…
The Exponentially Weighted Signature
Alexandre Bloch, Samuel N. Cohen, Terry Lyons +2
The signature is a canonical representation of a multidimensional path over an interval. However, it treats all historical information uniformly, offering no intrinsic mechanism fo…
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
Torben Berndt, Benjamin Walker, Tiexin Qin +2
Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential…