The tokens anchor these mathematical Forthe ofsource thetok batches representations.

Action knowledge involves the understanding of textual, visual, and temporal aspects of actions.

In contrast, we show that the computation of one PI-explanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration ofPI-explanations can beobtained with polynomial delay. Experimental results demonstrate the performance gains ofthe newalgorithms when compared with earlierwork.

Rather,theyare generated by latent variables, such as confounders and mediators, which may themselvesbecausallyrelated.

Moreover,doctors explicitly explore severepathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases.

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

A 2025 survey in the forests of Laos, Vietnam, and Cambodia uncovered several rare and endangered animals. A pig-tailed macaque is caught on camera in a Cambodian forest. Breakthroughs, discoveries, and DIY tips sent six days a week. The results of a new camera-trap survey in Southeast Asia is revealing a bevy of hidden biodiversity tucked within the Annamites mountain range . This largely unexplored wildlife hotspot has a forest stretching 683 miles (1,100 kilometers) across the countries of Laos, Vietnam, and Cambodia.

The $β$-VAE is a foundational framework for unsupervised disentanglement, using $β$ to regulate the trade-off between latent factorization and reconstruction fidelity. Empirically, however, disentanglement performance exhibits a pervasive non-monotonic trend: benchmarks such as MIG and SAP typically peak at intermediate $β$ and collapse as regularization increases. We demonstrate that this collapse is a fundamental information-theoretic failure, where strong Kullback-Leibler pressure promotes marginal independence at the expense of the latent channel's semantic informativeness. By formalizing this mechanism in a linear-Gaussian setting, we prove that for $β> 1$, stationarity-induced dynamics trigger a spectral contraction of the encoder gain, driving latent-factor mutual information to zero. To resolve this, we introduce the $λβ$-VAE, which decouples regularization pressure from informational collapse via an auxiliary $L_2$ reconstruction penalty $λ$. Extensive experiments on dSprites, Shapes3D, and MPI3D-real confirm that $λ> 0$ stabilizes disentanglement and restores latent informativeness over a significantly broader range of $β$, providing a principled theoretical justification for dual-parameter regularization in variational inference backbones.

As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how individual features influence model outcomes is crucial for building interpretable and equitable models. While feature importance metrics for accuracy are well-established, methods for assessing feature contributions to fairness remain underexplored. We propose two model-agnostic approaches to measure fair feature importance. First, we propose to compare model fairness before and after permuting feature values. This simple intervention-based approach decouples a feature and model predictions to measure its contribution to training. Second, we evaluate the fairness of models trained with and without a given feature. This occlusion-based score enjoys dramatic computational simplification via minipatch learning. Our empirical results reflect the simplicity and effectiveness of our proposed metrics for multiple predictive tasks. Both methods offer simple, scalable, and interpretable solutions to quantify the influence of features on fairness, providing new tools for responsible machine learning development.