基本信息
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Bio
My research focuses on reasoning, compositionality, and representation learning, at the intersection of vision and language.
I explore structural principles and inductive biases for making neural networks more interpretable, robust and data-efficient, and allow them to generalize effectively and systematically from a few samples only. I believe in the importance of multi-disciplinary both within the AI field and across domains, and draw high-level inspiration from the feats of the human mind, including its structural properties as well as cognitive capabilities.
I believe that compositionality is a key ingredient that, if incorporated successfully into neural models, may help bridging the gap between machine intelligence and natural intelligence. I explore ways to achieve compositionality both in terms of computation and representation.
I explore structural principles and inductive biases for making neural networks more interpretable, robust and data-efficient, and allow them to generalize effectively and systematically from a few samples only. I believe in the importance of multi-disciplinary both within the AI field and across domains, and draw high-level inspiration from the feats of the human mind, including its structural properties as well as cognitive capabilities.
I believe that compositionality is a key ingredient that, if incorporated successfully into neural models, may help bridging the gap between machine intelligence and natural intelligence. I explore ways to achieve compositionality both in terms of computation and representation.
Research Interests
Papers共 11 篇Author StatisticsCo-AuthorSimilar Experts
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SIMA Team, Maria Abi Raad,Arun Ahuja, Catarina Barros,Frederic Besse, Andrew Bolt,Adrian Bolton, Bethanie Brownfield,Gavin Buttimore, Max Cant, Sarah Chakera,Stephanie C. Y. Chan,
CoRR (2024)
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arxiv(2022)
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 (2021): 4487-4499
CoRR (2019): 5901-5914
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