19 citations · 26 across the 13 of their papers we have counts for
13 papers · 1 filter
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…
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…
Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models
Benjamin Walker, Lingyi Yang, Nicola Muca Cirone +2
This work introduces Structured Linear Controlled Differential Equations (SLiCEs), a unifying framework for sequence models with structured, input-dependent state-transition matric…
Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts
Felix Krones, Ben Walker, Terry Lyons +1
This work presents our team's (SignalSavants) winning contribution to the 2024 George B. Moody PhysioNet Challenge. The Challenge had two goals: reconstruct ECG signals from printo…
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest
Felix H. Krones, Ben Walker, Guy Parsons +2
This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac…
Theoretical Foundations of Deep Selective State-Space Models
Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker +2
Structured state-space models (SSMs) such as S4, stemming from the seminal work of Gu et al., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs…
Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference
Benjamin Walker, Andrew D. McLeod, Tiexin Qin +3
The vector field of a controlled differential equation (CDE) describes the relationship between a control path and the evolution of a solution path. Neural CDEs (NCDEs) treat time…
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem
Elena Gal, Shaun Singh, Aldo Pacchiano +3
In many real world settings binary classification decisions are made based on limited data in near real-time, e.g. when assessing a loan application. We focus on a class of these p…
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
Benjamin Walker, Felix Krones, Ivan Kiskin +3
This study presents our team PathToMyHeart's contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DB…
Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations
Tiexin Qin, Benjamin Walker, Terry Lyons +2
This paper focuses on representation learning for dynamic graphs with temporal interactions. A fundamental issue is that both the graph structure and the nodes own their own dynami…