118 citations · 253 across the 35 of their papers we have counts for
10 papers · 1 filter
Object-Centric Case-Based Reasoning via Argumentation
Gabriel de Olim Gaul, Adam Gould, Avinash Kori +1
We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a…
Transparent Visual Reasoning via Object-Centric Agent Collaboration
Benjamin Teoh, Ben Glocker, Francesca Toni +1
A central challenge in explainable AI, particularly in the visual domain, is producing explanations grounded in human-understandable concepts. To tackle this, we introduce OCEAN (O…
Flow Stochastic Segmentation Networks
Fabio De Sousa Ribeiro, Omar Todd, Charles Jones +3
We introduce the Flow Stochastic Segmentation Network (Flow-SSN), a generative segmentation model family featuring discrete-time autoregressive and modern continuous-time flow vari…
Object-Centric Neuro-Argumentative Learning
Abdul Rahman Jacob, Avinash Kori, Emanuele De Angelis +3
Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We i…
Diffusion Counterfactual Generation with Semantic Abduction
Rajat Rasal, Avinash Kori, Fabio De Sousa Ribeiro +2
Counterfactual image generation presents significant challenges, including preserving identity, maintaining perceptual quality, and ensuring faithfulness to an underlying causal mo…
Identifiable Object Representations under Spatial Ambiguities
Avinash Kori, Francesca Toni, Ben Glocker
Modular object-centric representations are essential for *human-like reasoning* but are challenging to obtain under spatial ambiguities, *e.g. due to occlusions and view ambiguitie…
Causal Representation Learning with Observational Grouping for CXR Classification
Rajat Rasal, Avinash Kori, Ben Glocker
Identifiable causal representation learning seeks to uncover the true causal relationships underlying a data generation process. In medical imaging, this presents opportunities to…
Factored Classifier-Free Guidance
Tian Xia, Fabio De Sousa Ribeiro, Rajat R Rasal +3
Counterfactual generation aims to simulate realistic hypothetical outcomes under causal interventions. Diffusion models have emerged as a powerful tool for this task, combining DDI…
Free Argumentative Exchanges for Explaining Image Classifiers
Avinash Kori, Antonio Rago, Francesca Toni
Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifier…
Explainable Reinforcement Learning for Formula One Race Strategy
Devin Thomas, Junqi Jiang, Avinash Kori +6
In Formula One, teams compete to develop their cars and achieve the highest possible finishing position in each race. During a race, however, teams are unable to alter the car, so…