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Showing papers from Jagiellonian University in Krakow Show all papers

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NeurIPS 2026Wroclaw TechJagiellonian University in KrakoJagiellonianWroclaw University of Science anExplainable AI

CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations

CounterFlowNet uses generative flow networks to generate sequential, sparse counterfactual explanations for tabular data with enforced actionability constraints and improved validity-sparsity trade-offs.

Oleksii Furman, Patryk Marszałek, Jan Masłowski, Marek Śmieja and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 4/5
medium 6/10
strict 0/5
74%Highly rated
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NeurIPS 2026Jagiellonian University in KrakoIDEAS Research Institute JagiellJagiellonianIDEAS Research InstituteRepresentation & contrastive learning

InTAct: Interval-based Task Activation Consolidation for Continual Learning

InTAct prevents catastrophic forgetting via neuron-level activation interval constraints providing functional invariance guarantees with greater efficiency than weight-space methods.

Patryk Krukowski, Jan Miksa, Piotr Helm, Jacek Tabor and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 4/5
medium 5/10
strict 0/5
78%Highly rated
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NeurIPS 2026U Edinburgh, United KingdomJagiellonian University, ul. GołJagiellonian University in KrakoJagiellonianExplainable AI

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data

B-XAIC benchmark evaluates explainable AI for graph neural networks on real molecular tasks with ground-truth rationales, revealing major limitations in current explanation methods.

Magdalena Proszewska, Tomasz Danel, Antoni Antoszek, Dawid Damian Rymarczyk

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 1/5