3 papers
cs.AI2026
Concept-based Visual Counterfactual Explanations with Diffusion Models
Yassine Oueslati, Daniil Kirilenko, Martin Gjoreski +1
Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?", and are increasingly important as vision models are deploye…
cs.LG2026
Federated Learning with Energy-Based Structured Probabilistic Inference
Dario Fenoglio, Daniil Kirilenko, Martin Gjoreski +1
Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributi…
cs.LG2023
Object-Centric Learning with Slot Mixture Module
Daniil Kirilenko, Vitaliy Vorobyov, Alexey K. Kovalev +1
Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots. Some of these methods…