Search Results for author: Raúl Santos-Rodríguez

Found 9 papers, 1 papers with code

Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information

no code implementations24 May 2019 Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie

Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data.

Dimensionality Reduction

Neural ODEs with stochastic vector field mixtures

no code implementations23 May 2019 Niall Twomey, Michał Kozłowski, Raúl Santos-Rodríguez

It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representations.

Ordinal Regression as Structured Classification

no code implementations31 May 2019 Niall Twomey, Rafael Poyiadzi, Callum Mann, Raúl Santos-Rodríguez

This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels.

Classification General Classification +1

Sampling Based On Natural Image Statistics Improves Local Surrogate Explainers

no code implementations8 Aug 2022 Ricardo Kleinlein, Alexander Hepburn, Raúl Santos-Rodríguez, Fernando Fernández-Martínez

By training a simple, more interpretable model to locally approximate the decision boundary of a non-interpretable system, we can estimate the relative importance of the input features on the prediction.

Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways

no code implementations7 Dec 2023 Michelle W. L. Wan, Jeffrey N. Clark, Edward A. Small, Elena Fillola Mayoral, Raúl Santos-Rodríguez

Sustainable global development is one of the most prevalent challenges facing the world today, hinging on the equilibrium between socioeconomic growth and environmental sustainability.

An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations

1 code implementation28 Mar 2024 Jonathan Erskine, Matt Clifford, Alexander Hepburn, Raúl Santos-Rodríguez

Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence.

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