Julia Siekiera

Julia Siekiera, Stefan Kramer

Artificial intelligence is increasingly leveraged across various domains to automate decision-making processes that significantly impact human lives. In medical image analysis, deep learning models have demonstrated remarkable performance. However, their inherent complexity makes them black box systems, raising concerns about reliability and interpretability. Counterfactual explanations provide comprehensible insights into decision processes by presenting hypothetical "what-if" scenarios that alter model classifications. By examining input alterations, counterfactual explanations provide patterns that influence the decision-making process. Despite their potential, generating plausible counterfactuals that adhere to similarity constraints providing human-interpretable explanations remains a challenge. In this paper, we investigate this challenge by a model-specific optimization approach. While deep generative models such as variational autoencoders (VAEs) exhibit significant generative power, probabilistic models like sum-product networks (SPNs) efficiently represent complex joint probability distributions. By modeling the likelihood of a semi-supervised VAE's latent space with an SPN, we leverage its dual role as both a latent space descriptor and a classifier for a given discrimination task. This formulation enables the optimization of latent space counterfactuals that are both close to the original data distribution and aligned with the target class distribution. We conduct experimental evaluation on the cheXpert dataset. To evaluate the effectiveness of the integration of SPNs, our SPN-guided latent space manipulation is compared against a neural network baseline. Additionally, the trade-off between latent variable regularization and counterfactual quality is analyzed.

Julia Siekiera, Stefan Kramer

The exploration of selected single nucleotide polymorphisms (SNPs) to identify genetic diversity between different sequencing population pools (Pool-seq) is a fundamental task in genetic research. As underlying sequence reads and their alignment are error-prone and univariate statistical solutions only take individual positions of the genome into account, the identification of selected SNPs remains a challenging process. Deep learning models like convolutional neural networks (CNNs) are able to consider large input areas in their decisions. We suggest an unsupervised pipeline to be independent of a rarely known ground truth. We train a supervised discriminator CNN to distinguish alignments from different populations and utilize the model for unsupervised SNP calling by applying explainable artificial intelligence methods. Our proposed multivariate method is based on two main assumptions: We assume (i) that instances having a high predictive certainty of being distinguishable are likely to contain genetic variants, and (ii) that selected SNPs are located at regions with input features having the highest influence on the model's decision process. We directly compare our method with statistical results on two different Pool-seq datasets and show that our solution is able to extend statistical results.

Julia Siekiera, Marius Köppel, Edwin Simpson et al.

The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require substantial amounts of labeled data that are difficult and expensive to obtain for different domains, languages and creative characteristics. A recent neural approach, the DirectRanker, promises to reduce the amount of training data needed but its application to text isn't fully explored. We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data. We compare DirectRanker with a Bayesian approach, Gaussian process preference learning (GPPL), which has previously been shown to work well with sparse data. Our experiments with sparse training data show that while the performance of standard neural ranking approaches collapses with small training datasets, DirectRanker remains effective. We find that combining DirectRanker with GPPL increases performance across different settings by leveraging the complementary benefits of both models. Our combined approach outperforms the previous state-of-the-art on humor and metaphor novelty tasks, increasing Spearman's $ρ$ by 14% and 16% on average.