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Found 5 papers, 1 papers with code
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
Counterfactual examples are an appealing class of post-hoc explanations for machine learning models.
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement
A key assumption of most statistical machine learning methods is that they have access to independent samples from the distribution of data they encounter at test time.
Making deep neural networks right for the right scientific reasons by interacting with their explanations
Deep neural networks have shown excellent performances in many real-world applications.
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks.
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions in a tractable fashion, but still lack the expressive power of intractable models based on deep neural networks.
