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.
no code implementations • 27 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.
2 code implementations • 25 Jun 2018 • Ryan McConville, Gareth Archer, Ian Craddock, Herman ter Horst, Robert Piechocki, James Pope, Raul Santos-Rodriguez
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.
no code implementations • 24 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.
no code implementations • 1 Jul 2019 • Xiaoyang Wang, Ioannis Mavromatis, Andrea Tassi, Raul Santos-Rodriguez, Robert J. Piechocki
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system.
no code implementations • 9 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.
5 code implementations • 16 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.
Ranked #1 on Image Clustering on pendigits
3 code implementations • 11 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.
1 code implementation • 20 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).
no code implementations • 28 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.
1 code implementation • 29 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).
no code implementations • 3 Jul 2020 • Rafael Poyiadzi, Weisong Yang, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raul Santos-Rodriguez, James Selwood, Niall Twomey
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia.
no code implementations • 22 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.
no code implementations • 3 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.
no code implementations • 3 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.
no code implementations • 19 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.
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.
no code implementations • 10 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.
no code implementations • 9 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.
1 code implementation • 8 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.
no code implementations • 16 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.
no code implementations • 17 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.
no code implementations • 20 Dec 2021 • Telmo Silva Filho, Hao Song, Miquel Perello-Nieto, Raul Santos-Rodriguez, Meelis Kull, Peter Flach
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration.
no code implementations • 30 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.
no code implementations • 15 Aug 2022 • Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville
We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion.
no code implementations • 8 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.
no code implementations • 8 Sep 2022 • Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raul Santos-Rodriguez, Peter Flach
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life.
no code implementations • 8 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.
no code implementations • 6 Nov 2022 • Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry
We see that OOD samples tend to be classified into classes that have a distribution similar to the distribution of the entire dataset.
no code implementations • 19 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.
no code implementations • 20 Jan 2023 • Amarpal Sahota, Amber Roguski, Matthew W. Jones, Michal Rolinski, Alan Whone, Raul Santos-Rodriguez, Zahraa S. Abdallah
Detecting Parkinson's Disease in its early stages using EEG data presents a significant challenge.
no code implementations • 31 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.
no code implementations • 6 Feb 2023 • Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perello Nieto, Raul Santos-Rodriguez, Weisong Yang, Peter Flach
Here we propose a method to model human biases on temporal annotations and argue for the use of soft labels.
no code implementations • 10 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.
no code implementations • 19 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.
no code implementations • 19 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.
no code implementations • 8 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.
1 code implementation • 27 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.
no code implementations • 13 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.
no code implementations • 6 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.
no code implementations • 15 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.
no code implementations • 27 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.