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Ishita Dasgupta
Ishita Dasgupta
Research Scientist, DeepMind
Email confirmado em google.com - Página inicial
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Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
34172023
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
13052024
Can language models learn from explanations in context?
AK Lampinen, I Dasgupta, SCY Chan, K Matthewson, MH Tessler, ...
Findings of the Association for Computational Linguistics: EMNLP 2022, 537--563, 2022
2912022
Are Convolutional Neural Networks or Transformers more like human vision?
S Tuli, I Dasgupta, E Grant, TL Griffiths
Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43), 2021
2492021
Language models show human-like content effects on reasoning tasks
I Dasgupta, AK Lampinen, SCY Chan, HR Sheahan, A Creswell, ...
arXiv preprint arXiv:2207.07051, 2022
2142022
Where do hypotheses come from?
I Dasgupta, E Schulz, SJ Gershman
Cognitive psychology 96, 1-25, 2017
1542017
Evaluating compositionality in sentence embeddings
I Dasgupta, D Guo, A Stuhlmüller, SJ Gershman, ND Goodman
Proceedings of the Annual Meeting of the Cognitive Science Society 40 (40), 2018
1412018
Causal reasoning from meta-reinforcement learning
I Dasgupta, J Wang, S Chiappa, J Mitrovic, P Ortega, D Raposo, ...
arXiv preprint arXiv:1901.08162, 2019
1352019
A theory of learning to infer.
I Dasgupta, E Schulz, JB Tenenbaum, SJ Gershman
Psychological review 127 (3), 412, 2020
1112020
Pivot: Iterative visual prompting elicits actionable knowledge for vlms
S Nasiriany, F Xia, W Yu, T Xiao, J Liang, I Dasgupta, A Xie, D Driess, ...
arXiv preprint arXiv:2402.07872, 2024
862024
Remembrance of inferences past: Amortization in human hypothesis generation
I Dasgupta, E Schulz, ND Goodman, SJ Gershman
Cognition 178, 67-81, 2018
842018
Collaborating with language models for embodied reasoning
I Dasgupta, C Kaeser-Chen, K Marino, A Ahuja, S Babayan, F Hill, ...
NeurIPS 2022 Language and Reinforcement Learning Workshop, 2022
712022
Memory as a computational resource
I Dasgupta, SJ Gershman
Trends in cognitive sciences 25 (3), 240-251, 2021
692021
Using natural language and program abstractions to instill human inductive biases in machines
S Kumar, CG Correa, I Dasgupta, R Marjieh, MY Hu, R Hawkins, ...
Advances in Neural Information Processing Systems 35, 167-180, 2022
532022
Meta-learned models of cognition
M Binz, I Dasgupta, AK Jagadish, M Botvinick, JX Wang, E Schulz
Behavioral and Brain Sciences 47, e147, 2024
452024
Tell me why! explanations support learning relational and causal structure
AK Lampinen, N Roy, I Dasgupta, SCY Chan, A Tam, J Mcclelland, C Yan, ...
International Conference on Machine Learning, 11868-11890, 2022
452022
Transformers generalize differently from information stored in context vs in weights
SCY Chan, I Dasgupta, J Kim, D Kumaran, AK Lampinen, F Hill
Memory in Artificial and Real Intelligence (MemARI) workshop NeurIPS 2022, 2022
402022
Language models, like humans, show content effects on reasoning tasks
AK Lampinen, I Dasgupta, SCY Chan, HR Sheahan, A Creswell, ...
PNAS nexus 3 (7), pgae233, 2024
292024
Scaling instructable agents across many simulated worlds
M Abi Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ...
arXiv e-prints, arXiv: 2404.10179, 2024
29*2024
Meta-Learning of Structured Task Distributions in Humans and Machines
S Kumar, I Dasgupta, JD Cohen, ND Daw, TL Griffiths
International Conference on Learning Representations, 2021, 2020
27*2020
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