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个人简介
My research interests lie at the intersection of machine learning, psychology, and neuroscience. Broadly speaking, I am interested in understanding the mechanistic underpinnings of biological intelligence: what are the computational principles and circuit-level mechanisms that underlie our remarkable cognitive and perceptual capabilities? I approach this question by developing neural network models of cognitive and perceptual phenomena and by analyzing the behavior of deep learning models with the goal of understanding their similarities to, and differences from, humans.
Recent progress in machine learning has opened up exciting new possibilities for addressing some of the most fundamental questions at the intersection of brain and cognitive sciences and machine learning. These include questions such as the amount and type of data, and the inductive biases necessary to learn sophisticated world models, robust, general-purpose perceptual, and semantic representations. We can nowadays routinely train very large models on large, complex datasets and probe the capabilities of the resulting systems in fine detail. This gives us a vastly improved capacity to formulate and test innovative ideas. I try to utilize this capacity in my current work.
I like working on general algorithms and models that can scale to large (ideally, unlimited) real-world data, as opposed to bespoke algorithms and models tailored to simplistic tasks and environments.
Recent progress in machine learning has opened up exciting new possibilities for addressing some of the most fundamental questions at the intersection of brain and cognitive sciences and machine learning. These include questions such as the amount and type of data, and the inductive biases necessary to learn sophisticated world models, robust, general-purpose perceptual, and semantic representations. We can nowadays routinely train very large models on large, complex datasets and probe the capabilities of the resulting systems in fine detail. This gives us a vastly improved capacity to formulate and test innovative ideas. I try to utilize this capacity in my current work.
I like working on general algorithms and models that can scale to large (ideally, unlimited) real-world data, as opposed to bespoke algorithms and models tailored to simplistic tasks and environments.
研究兴趣
论文共 41 篇作者统计合作学者相似作者
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CoRR (2024)
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arXiv (Cornell University)no. 3 (2024): 271-283
Scienceno. 6682 (2024): 504-511
Cognition (2024): 105690-105690
arXiv (Cornell University) (2023)
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arxiv(2022)
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