Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data generation process.

Our solution is application agnostic and relies only on the data collected for diagnosis. For the evaluation, we compare the proposed solution with amodified version of the PC algorithm and the state-of-the-art for root cause analysis.

Y The constraint P X,P Y Prob(X) states that P X and P Y are valid probability distributions. For brevity, we shall ignore explicitly stating it in the rest of the proof. The above equation is similar in spirit to the Kantrovich-Rubinstein duality. An important observation to note is that the above optimization only maximizes over a single discriminator function (as opposed to two functions in optimization (2)). Hence, it is easier to train it in large-scale deep learningproblemssuchasGANs.

Robust XAI techniques should ideally be simultaneously deterministic, model agnostic, and guaranteed to converge. We propose MULTIDIMENSIONAL PROBLEM AGNOSTIC EXPLAINABLE AI (MUPAX), a deterministic, model agnostic explainability technique, with guaranteed convergency. MUPAX measure theoretic formulation gives principled feature importance attribution through structured perturbation analysis that discovers inherent input patterns and eliminates spurious relationships. We evaluate MUPAX on an extensive range of data modalities and tasks: audio classification (1D), image classification (2D), volumetric medical image analysis (3D), and anatomical landmark detection, demonstrating dimension agnostic effectiveness. The rigorous convergence guarantees extend to any loss function and arbitrary dimensions, making MUPAX applicable to virtually any problem context for AI. By contrast with other XAI methods that typically decrease performance when masking, MUPAX not only preserves but actually enhances model accuracy by capturing only the most important patterns of the original data. Extensive benchmarking against the state of the XAI art demonstrates MUPAX ability to generate precise, consistent and understandable explanations, a crucial step towards explainable and trustworthy AI systems. The source code will be released upon publication.

Tens of thousands of video game diehards are converging on the Boston Convention & Exhibition Center this weekend for the annual PAX East convention. Dressed to the nines as characters universal and niche, gaming buffs will line up to play the newest games, watch professionals duke it out on the big screen and more. Here are the top things to look for if you go to PAX this year. Throughout the convention, a group of professional gamers will be competing to win championships in everything from NBA2K to Mario Kart. The New England Revolution are putting on a video game version of the MLS Cup that will be played through the weekend.