Dasgupta, Ishita

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Can language models learn from explanations in context?

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 163

Can language models learn from explanations in context?

Lampinen, A., Dasgupta, I., Chan, S., Mathewson, K., Tessler, M., Creswell, A., McClelland, J., Wang, J., & Hill, F. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingNatural Language Processing Techniques

Cited by 142

Are Convolutional Neural Networks or Transformers more like human vision?

Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 119

Causal Reasoning from Meta-reinforcement Learning

Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M., & Kurth-Nelson, Z. (2019) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 71

A theory of learning to infer.

Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020) · Psychological Review

Bayesian Modeling and Causal InferenceDecision-Making and Behavioral Economics

Cited by 70

Language models, like humans, show content effects on reasoning tasks

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2024) · PNAS Nexus

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 69

Language models show human-like content effects on reasoning tasks

Dasgupta, I., Lampinen, A. K., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2022) · arXiv

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 53

Meta-learned models of cognition

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences

Cognitive Science and Education ResearchBayesian Modeling and Causal Inference

Cited by 38

Collaborating with language models for embodied reasoning

Dasgupta, I., Kaeser-Chen, C., Marino, K., Ahuja, A., Babayan, S., Hill, F., & Fergus, R. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 14

Tell me why! Explanations support learning relational and causal structure

Lampinen, A. K., Roy, N. A., Dasgupta, I., Chan, S. C. Y., Tam, A. C., McClelland, J. L., Yan, C., Santoro, A., Rabinowitz, N. C., Wang, J. X., et al. (2021) · arXiv

Reinforcement Learning in RoboticsExplainable Artificial Intelligence (XAI)

Cited by 14

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Nasiriany, S., Xia, F., Yu, W., Xiao, T., Liang, J., Dasgupta, I., Xie, A., Driess, D., Wahid, A., Xu, Z., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsRobot Manipulation and Learning

Cited by 13

Transformers generalize differently from information stored in context vs in weights

Chan, S. C. Y., Dasgupta, I., Kim, J., Kumaran, D., Lampinen, A. K., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 11

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R., Cohen, J. D., Daw, N., Narasimhan, K., & Griffiths, T. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 10

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

Kumar, S., Dasgupta, I., Daw, N. D., Cohen, J. D., & Griffiths, T. L. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Raad, M. A., Ahuja, A., Barros, C., Besse, F., Bolt, A., Bolton, A., Brownfield, B., Buttimore, G., Cant, M., et al. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 9

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R. D., Daw, N. D., Cohen, J. D., Narasimhan, K., & Griffiths, T. L. (2022) · arXiv

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 9

Amortized Hypothesis Generation

Dasgupta, I., Schulz, E., Goodman, N. D., & Gershman, S. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory)

Bayesian Modeling and Causal InferenceCognitive Science and Education Research

Cited by 7

How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?

Liu, R., Sumers, T. R., Dasgupta, I., & Griffiths, T. L. (2024) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 1