18 citations · 47 across the 16 of their papers we have counts for
16 papers
SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation
Petros Andreou, Jamie Lanyon, Axel Finke +1
Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch. Evaluating an unlearning method requires repeating train…
RULER: Representation-Level Verification of Machine Unlearning
Georgina Cosma, Axel Finke
Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current protocols verify this at the output leve…
On the importance of multiple training seeds for evaluating machine unlearning
Jamie Lanyon, Axel Finke, Petros Andreou +1
Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining. Most practical unlearning algorithms are only approximate and…
Neural Corrective Machine Unranking
Jingrui Hou, Axel Finke, Georgina Cosma
Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods t…
Neural Machine Unranking
Jingrui Hou, Axel Finke, Georgina Cosma
We address the problem of machine unlearning in neural information retrieval (IR), introducing a novel task termed Neural Machine UnRanking (NuMuR). This problem is motivated by gr…
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
Jingrui Hou, Georgina Cosma, Axel Finke
Continual learning refers to the capability of a machine learning model to learn and adapt to new information, without compromising its performance on previously learned tasks. Alt…
Neural-based Cross-modal Search and Retrieval of Artwork
Yan Gong, Georgina Cosma, Axel Finke
Creating an intelligent search and retrieval system for artwork images, particularly paintings, is crucial for documenting cultural heritage, fostering wider public engagement, and…
Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis
Eufrásio de A. Lima Neto, Jonathan Bailiss, Axel Finke +2
Local authorities in England, such as Leicestershire County Council (LCC), provide Early Help services that can be offered at any point in a young person's life when they experienc…
Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models
Jiajun Zhang, Georgina Cosma, Sarah Bugby +2
As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent r…
VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval
Yan Gong, Georgina Cosma, Axel Finke
The relations expressed in user queries are vital for cross-modal information retrieval. Relation-focused cross-modal retrieval aims to retrieve information that corresponds to the…
A Bayesian framework for genome-wide inference of DNA methylation levels
Marcel Hirt, Axel Finke, Alexandros Beskos +4
DNA methylation is an important epigenetic mark that has been studied extensively for its regulatory role in biological processes and diseases. WGBS allows for genome-wide measurem…
Conditional sequential Monte Carlo in high dimensions
Axel Finke, Alexandre H. Thiery
The iterated conditional sequential Monte Carlo (i-CSMC) algorithm from Andrieu, Doucet and Holenstein (2010) is an MCMC approach for efficiently sampling from the joint posterior…
On importance-weighted autoencoders
Axel Finke, Alexandre H. Thiery
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than stan…
Limit theorems for sequential MCMC methods
Axel Finke, Arnaud Doucet, Adam M. Johansen
Sequential Monte Carlo (SMC) methods, also known as particle filters, constitute a class of algorithms used to approximate expectations with respect to a sequence of probability di…
On embedded hidden Markov models and particle Markov chain Monte Carlo methods
Axel Finke, Arnaud Doucet, Adam M. Johansen
The embedded hidden Markov model (EHMM) sampling method is a Markov chain Monte Carlo (MCMC) technique for state inference in non-linear non-Gaussian state-space models which was p…
Approximate Smoothing and Parameter Estimation in High-Dimensional State-Space Models
Axel Finke, Sumeetpal S. Singh
We present approximate algorithms for performing smoothing in a class of high-dimensional state-space models via sequential Monte Carlo methods ("particle filters"). In high dimens…