Sequence Classification using Ensembles of Recurrent Generative Expert Modules. - Marius Hobbhahn; Martin V. Butz; Sarah Fabi; Sebastian Otte (ESANN, 2020)

Investigating Efficient Learning and Compositionality in Generative LSTM Networks. - Sarah Fabi; Sebastian Otte; Jonas Gregor Wiese; Martin V. Butz (ICANN (1), 2020)

Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge. - Sarah Fabi; Sebastian Otte; Martin V. Butz (ICANN (2), 2021)

Efficient learning through compositionality in a CNN-RNN model consisting of a bottom-up and a top-down pathway. - Sarah Fabi; Lena Holzwarth; Martin V. Butz (CogSci, 2022)

Exploring the Racial Bias in Pain Detection with a Computer Vision Model. - Sarah Fabi; Xiaojing Xu; Virginia de Sa (CogSci, 2022)

Why we need biased AI - How including cognitive and ethical machine biases can enhance AI systems. - Sarah Fabi; Thilo Hagendorff (CoRR, 2022)

Machine intuition: Uncovering human-like intuitive decision-making in GPT-3.5. - Thilo Hagendorff; Sarah Fabi; Michal Kosinski (CoRR, 2022)

Methodological reflections for AI alignment research using human feedback. - Thilo Hagendorff; Sarah Fabi (CoRR, 2023)

Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. - Thilo Hagendorff; Sarah Fabi; Michal Kosinski (Nat. Comput. Sci., 2023)

Extending the Omniglot Challenge: Imitating Handwriting Styles on a New Sequential Data Set. - Sarah Fabi; Sebastian Otte; Fedor Scholz; Julius Wührer; Matthias Karlbauer; Martin V. Butz (IEEE Trans. Cogn. Dev. Syst., 2023)

Why we need biased AI: How including cognitive biases can enhance AI systems. - Thilo Hagendorff; Sarah Fabi (J. Exp. Theor. Artif. Intell., 2024)

Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models. - Thilo Hagendorff; Sarah Fabi (CoRR, 2025)