Publikationen von AK Jagadish

Ben-Zion; Witte, K.; Jagadish, A.; Duek, O.; Harpaz-Rotem, I.; Khorsandian, M.-C.; Burrer, A.; Seifritz, E.; Homann, P.; Schulz, E. et al.; Spiller, T.: Assessing and alleviating state anxiety in large language models. npj digital medicine 8 (1), 132 (2025)

Binz, M.; Dasgupta, I.; Jagadish, A.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences 47, e170 (2024)

Binz, M.; Dasgupta, I.; Jagadish, A.; Botvinick, M.; Wang, J.; Schulz, E.: Meta-Learned Models of Cognition. Behavioral and Brain Sciences 47, e147 (2024)

Rodriguez, C.; Jagadish, A.; Meskaldji, D.-E.; Haller, S.; Herrmann, F.; Van De Ville, D.; Giannakopoulos, P.: Structural Correlates of Personality Dimensions in Healthy Aging and MCI. Frontiers in Psychology 9, 2652, S. 1 - 13 (2019)

Jagadish, A.; Dasgupta, I.; Lerousseau, J.; Binz, M.: In-context learning in natural and artificial intelligence. In: 46th Annual Conference of the Cognitive Science Society (CogSci 2024), S. 6 - 7. In-Context Learning in Natural and Artificial Intelligence: Workshop at CogSci 2024, Rotterdam, The Netherlands, 24. Juli 2024. (2024)

Jagadish, A.; Coda-Forno, J.; Thalmann, M.; Schulz, E.; Binz, M.: Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks. In: Proceedings of Machine Learning Research: PLMR, Bd. 235, S. 21121 - 21147. 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, 21. Juli 2024 - 27. Juli 2024. (2024)

Schubert, J.; Jagadish, A.; Binz, M.; Schulz, E.: In-Context Learning Agents Are Asymmetric Belief Updaters. In: Proceedings of Machine Learning Research: PLMR, Bd. 235, S. 43928 - 43946. 41st International Conference on Machine Learning (ICML 2024), Wien, Austria, 21. Juli 2024 - 27. Juli 2024. (2024)

Schubert, J.; Jagadish, A.; Binz, M.; Schulz, E.: A Rational Analysis of the Optimism Bias using Meta-Reinforcement Learning. In: 2023 Conference on Cognitive Computational Neuroscience, P-2.37, S. 557 - 559. Conference on Cognitive Computational Neuroscience (CCN 2023), Oxford, UK, 24. August 2023 - 27. August 2023. (2023)

Jagadish, A.; Saanum, T.; Wang, J.; Binz, M.; Schulz, E.: Probing Compositional Inference in Natural and Artificial Agents. In: 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), 1.67, S. 275 - 279. 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2022), Providence, RI, USA, 08. Juni 2022 - 11. Juni 2022. (2022)

Bashiri, M.; Walker, E.; Lurz, K.-K.; Jagadish, A.; Muhammad, T.; Ding, Z.; Ding, Z.; Tolias, A.; Sinz, F.: A flow-based latent state generative model of neural population responses to natural images. In: Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021), S. 15801 - 15815 (Hg. Ranzato, M.; Beygelzimer, A.; Liang, P.; Vaughan, J.; Dauphin, Y.). Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), 06. Dezember 2021 - 14. Dezember 2021. Curran, Red Hook, NY, USA (2022)

Lurz, K.-K.; Bashiri, M.; Willeke, K.; Jagadish, A.; Wang, E.; Walker, E.; Cadena, S.; Muhammad, T.; Cobos, E.; Tolias, A. et al.; Ecker, A.; Sinz, F.: Generalization in data-driven models of primary visual cortex. In: Ninth International Conference on Learning Representations (ICLR 2021). Ninth International Conference on Learning Representations (ICLR 2021), Wien, Austria, 03. Mai 2021 - 07. Mai 2021. (2021)

Chen, H.; Jagadish, A.; Wenzel, O.: Causality in neuroscience. In 20th Conference of Junior Neuroscientists (NeNa 2019), W1, S. 50. 20th Conference of Junior Neuroscientists (NeNa 2019), Schramberg, Germany, 04. November 2019 - 06. November 2019. (2019)

Bashiri, M.; Walker, E.; Lurz, K.-K.; Jagadish, A.; Muhammad, T.; Ding, Z.; Tolias, A.; Sinz, F.: A flow-based latent state generative model of neural population responses to natural images. Bernstein Conference 2021 (2021)

Rmus, M.; Jagadish, A.; Mathony, M.; Ludwig, T.; Schulz, E.: Towards Automation of Cognitive Modeling using Large Language Models. (eingereicht)

Binz, M.; Akata, E.; Bethge, M.; Brändle, F.; Callaway, F.; Coda-Forno, J.; Dayan, P.; Demircan, C.; Eckstein, M.; Éltetö, N. et al.; Griffiths, T.; Haridi, S.; Jagadish, A.; Ji-An; Kipnis, A.; Kumar, S.; Ludwig, T.; Mathony, M.; Mattar, M.; Modirshanechi, A.; Nath, S.; Peterson, J.; Rmus, M.; Russek, E.; Saanum, T.; Scharfenberg, N.; Schubert, J.; Schulze Buschoff, L.; Singhi, N.; Sui, X.; Thalmann, M.; Theis, F.; Truong, V.; Udandarao, V.; Voudouris, K.; Wilson, R.; Witte, K.; Wu, S.; Wulff, D.; Xiong, H.; Schulz, E.: Centaur: a foundation model of human cognition. (eingereicht)

Demircan, C.; Saanum, T.; Jagadish, A.; Binz, M.; Schulz, E.: Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models. (eingereicht)

Jagadish, A.; Binz, M.; Saanum, T.; Wang, J.; Schulz, E.: Zero-shot compositional reinforcement learning in humans. (eingereicht)

Coda-Forno, J.; Witte, K.; Jagadish, A.; Binz, M.; Akata, Z.; Schulz, E.: Inducing anxiety in large language models increases exploration and bias. (eingereicht)