3 citations · 7 across the 5 of their papers we have counts for
5 papers
Rough Transformers for Continuous and Efficient Time-Series Modelling
Fernando Moreno-Pino, Álvaro Arroyo, Harrison Waldon +2
Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In such contexts, traditional sequence-based re…
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent research indicates that the performance of machine learning models can be improved by aligning the geometry of the latent space with the underlying data structure. Rather th…
Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces
Haitz Saez de Ocariz Borde, Alvaro Arroyo, Ismael Morales +2
Recent studies propose enhancing machine learning models by aligning the geometric characteristics of the latent space with the underlying data structure. Instead of relying solely…
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
Alvaro Arroyo, Alvaro Cartea, Fernando Moreno-Pino +1
One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order…
Projections of Model Spaces for Latent Graph Inference
Haitz Sáez de Ocáriz Borde, Álvaro Arroyo, Ingmar Posner
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse informat…