Search Results for author: Raul Santos-Rodriguez

Found 42 papers, 7 papers with code

Meta-song evaluation for chord recognition

no code implementations TBD 2011 Yizhao Ni, Matt Mcvicar, Raul Santos-Rodriguez, Tijl De Bie

We present a new approach to evaluate chord recognition systems on songs which do not have full annotations.

Chord Recognition

Informative Data Projections: A Framework and Two Examples

no code implementations27 Nov 2015 Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez, Bo Kang

Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.

Vocal Bursts Valence Prediction

Label Propagation for Learning with Label Proportions

no code implementations24 Oct 2018 Rafael Poyiadzi, Raul Santos-Rodriguez, Niall Twomey

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags.

Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance

no code implementations9 Aug 2019 Alexander Hepburn, Valero Laparra, Ryan McConville, Raul Santos-Rodriguez

While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked.

Image Segmentation Image-to-Image Translation +2

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

5 code implementations16 Aug 2019 Ryan McConville, Raul Santos-Rodriguez, Robert J. Piechocki, Ian Craddock

We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.

Clustering Deep Clustering +4

FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

3 code implementations11 Sep 2019 Kacper Sokol, Raul Santos-Rodriguez, Peter Flach

Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives.

BIG-bench Machine Learning Fairness +1

FACE: Feasible and Actionable Counterfactual Explanations

1 code implementation20 Sep 2019 Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, Peter Flach

First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).

counterfactual

PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance

no code implementations28 Oct 2019 Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, Raul Santos-Rodriguez

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations.

Perceptual Distance

bLIMEy: Surrogate Prediction Explanations Beyond LIME

1 code implementation29 Oct 2019 Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez, Peter Flach

Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i. e., can be retrofitted).

Explainable artificial intelligence

Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception

no code implementations22 Feb 2021 Alexander Hepburn, Raul Santos-Rodriguez

We generate explanations for images in the Imagenet-C dataset and demonstrate how using a perceptual distances in the surrogate explainer creates more coherent explanations for the distorted and reference images.

Image Classification

Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

no code implementations3 Mar 2021 Xiaoyang Wang, Jonathan D Thomas, Robert J Piechocki, Shipra Kapoor, Raul Santos-Rodriguez, Arjun Parekh

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC.

Edge-computing Management

Hypothesis Testing for Class-Conditional Label Noise

no code implementations3 Mar 2021 Rafael Poyiadzi, Weisong Yang, Niall Twomey, Raul Santos-Rodriguez

Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2.

Self-Supervised WiFi-Based Activity Recognition

no code implementations19 Apr 2021 Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus, Robert J. Piechocki, Raul Santos-Rodriguez

Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities.

Activity Recognition Contrastive Learning +1

On the relation between statistical learning and perceptual distances

no code implementations ICLR 2022 Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez, Johannes Ballé, Jesús Malo

Since machine learning relies on the statistics of training data as well, the above connection has interesting implications when using perceptual distances (which mimic the behavior of the human visual system) as a loss function.

BIG-bench Machine Learning Perceptual Distance +1

On the overlooked issue of defining explanation objectives for local-surrogate explainers

no code implementations10 Jun 2021 Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raul Santos-Rodriguez, Marcin Detyniecki

In this work we review the similarities and differences amongst multiple methods, with a particular focus on what information they extract from the model, as this has large impact on the output: the explanation.

Understanding surrogate explanations: the interplay between complexity, fidelity and coverage

no code implementations9 Jul 2021 Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raul Santos-Rodriguez, Marcin Detyniecki

This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings.

OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors

1 code implementation8 Oct 2021 Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki

This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.

Human Activity Recognition Multimodal Activity Recognition

Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills

no code implementations16 Nov 2021 Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez

We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.

reinforcement-learning Reinforcement Learning (RL)

Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach

no code implementations17 Nov 2021 Jonas Schulz, Rafael Poyiadzi, Raul Santos-Rodriguez

To this end, we produce estimates of the uncertainty of a given explanation by measuring the ordinal consensus amongst a set of diverse bootstrapped surrogate explainers.

Open-Ended Question Answering Uncertainty Quantification

The Weak Supervision Landscape

no code implementations30 Mar 2022 Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro, Miquel Perello-Nieto, Peter Flach, Raul Santos-Rodriguez

In this paper we propose a framework for categorising weak supervision settings with the aim of: (1) helping the dataset owner or annotator navigate through the available options within weak supervision when prescribing an annotation process, and (2) describing existing annotations for a dataset to machine learning practitioners so that we allow them to understand the implications for the learning process.

BIG-bench Machine Learning Navigate

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components

no code implementations8 Sep 2022 Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez, Peter Flach

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable.

Explanation Generation

Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL

no code implementations8 Sep 2022 Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez

The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks.

D4RL Offline RL +2

Identification, explanation and clinical evaluation of hospital patient subtypes

no code implementations19 Jan 2023 Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber, Michael Ambler, Christopher P. Bourdeaux, Alexander Hepburn, Christopher J. McWilliams, Raul Santos-Rodriguez

We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital.

Clinical Knowledge

Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease

no code implementations31 Jan 2023 Maha M. Alwuthaynani, Zahraa S. Abdallah, Raul Santos-Rodriguez

Motivated by these approaches, this paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images.

Image Classification Medical Image Classification +1

Two-step counterfactual generation for OOD examples

no code implementations10 Feb 2023 Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry

Two fundamental requirements for the deployment of machine learning models in safety-critical systems are to be able to detect out-of-distribution (OOD) data correctly and to be able to explain the prediction of the model.

counterfactual Out of Distribution (OOD) Detection +1

MIDI-Draw: Sketching to Control Melody Generation

no code implementations19 May 2023 Tashi Namgyal, Peter Flach, Raul Santos-Rodriguez

We describe a proof-of-principle implementation of a system for drawing melodies that abstracts away from a note-level input representation via melodic contours.

What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics

no code implementations19 May 2023 Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez, Valero Laparra, Jesus Malo

In this study, we investigate the feasibility of utilizing state-of-the-art image perceptual metrics for evaluating audio signals by representing them as spectrograms.

Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse

no code implementations8 Sep 2023 Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams, Kacper Sokol, Jeffrey Chan, Flora D. Salim, Raul Santos-Rodriguez

Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.

counterfactual

TraCE: Trajectory Counterfactual Explanation Scores

1 code implementation27 Sep 2023 Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand, Michelle W. L. Wan, Elena Fillola Mayoral, Enrico Werner, Christopher P. Bourdeaux, Raul Santos-Rodriguez

Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier.

counterfactual Counterfactual Explanation +1

LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations

no code implementations13 Oct 2023 Ahmed Khalil, Robert Piechocki, Raul Santos-Rodriguez

In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations.

Quantization

Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data

no code implementations6 Dec 2023 Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez, Valero Laparra, Jesus Malo

Perceptual metrics are traditionally used to evaluate the quality of natural signals, such as images and audio.

Evaluating Perceptual Distances by Fitting Binomial Distributions to Two-Alternative Forced Choice Data

no code implementations15 Mar 2024 Alexander Hepburn, Raul Santos-Rodriguez, Javier Portilla

The two-alternative forced choice (2AFC) experimental setup is popular in the visual perception literature, where practitioners aim to understand how human observers perceive distances within triplets that consist of a reference image and two distorted versions of that image.

Decision Making Perceptual Distance

Safe and Robust Reinforcement-Learning: Principles and Practice

no code implementations27 Mar 2024 Taku Yamagata, Raul Santos-Rodriguez

Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness.

Domain Adaptation reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.