Publikationen von M Binz

Binz, M.; Schulz, E.: Modeling Human Exploration Through Resource-Rational Reinforcement Learning. In: Advances in Neural Information Processing Systems 35: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), S. 31755 - 31768 (Hg. Koyejo, S.; Mohamed, S.). Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, USA, 28. November 2022 - 09. Dezember 2022. Curran, Red Hook, NY, USA (2023)

Demircan, C.; Pettini, L.; Saanum, T.; Binz, M.; Baczkowski, B.; Doeller, C.; Garvert, M.; Schulz, E.: Decision-Making with Naturalistic Options. In: 44th Annual Meeting of the Cognitive Science Society (CogSci 2022): Cognitive Diversity, S. 976 - 982 (Hg. Culbertson, J.; Perfors, A.; Rabagliati, H.; Ramenzoni, V.). 44th Annual Meeting of the Cognitive Science Society (CogSci 2022): Cognitive Diversity, Toronto, Canada, 27. Juli 2022 - 30. Juli 2022. (2022)

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)

Binz, M.; Schulz, E.: Using cognitive psychology to understand GPT-3. In 29th Biennial Subjective Probability Utility and Decision Making Conference (SPUDM 2023), S. 31. 29th Biennial Subjective Probability Utility and Decision Making Conference (SPUDM 2023), Wien, Austria, 20. August 2023 - 24. August 2023. (2023)

Binz, M.: Building foundation models of human cognition. Practice Job Talk, Tübingen, Germany (2023)

Tutorial 2: Meta-Learned Models of Cognition. 15th Biannual Conference of the German Society for Cognitive Science (KogWis 2022), Freiburg (Breisgau), Germany (2022)

Truong, V.; Binz, M.; Bartels, A.: Fear and anxiety influences on probabilistic learning: A pilot online study and computational modeling. 23rd Conference of Junior Neuroscientists (NeNa 2022), Bad Urach, Germany (2022)

Jagadish, A.; Thalmann, M.; Coda-Forno, J.; Binz, M.; Schulz, E.: Meta-learning ecological priors from large language models explains human learning and decision making. (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)

Coda-Forno, J.; Binz, M.; Wang, J.; Schulz, E.: CogBench: a large language model walks into a psychology lab. (eingereicht)

Binz, M.; Alaniz, S.; Roskies, A.; Aczel, B.; Bergstrom, C.; Allen, C.; Schad, D.; Wulff, D.; West, J.; Zhang, Q. et al.; Shriffrin, R.; Gershman, S.; Popov, V.; Bender, E.; Marelli, M.; Botvinick, M.; Akata, Z.; Schulz, E.: How should the advent of large language models affect the practice of science? (eingereicht)

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

Demircan, C.; Saanum, T.; Pettini, L.; Binz, M.; Baczkowski, B.; Kaanders, P.; Doeller, C.; Garvert, M.; Schulz, E.: Language Aligned Visual Representations Predict Human Behavior in Naturalistic Learning Tasks. (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)

Binz, M.; Schulz, E.: Modeling Human Exploration Through Resource-Rational Reinforcement Learning. (eingereicht)

Schulze Buschoff, L.; Schulz, E.; Binz, M.: Stochastic Gradient Descent Captures How Children Learn About Physics. (eingereicht)

Binz, M.; Schulz, E.: Exploration With a Finite Brain. (eingereicht)