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Daniel Yamins
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Daniel Yamins
@dyamins
CS, psych, and neuro prof @ Stanford. NeuroAI and "regular AI". Also harpsichords and bonsai. danyamins.substack.com
Stanford, CA
neuroailab.stanford.edu
Joined April 2009
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  • Pinned
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    Daniel Yamins
    @dyamins
    Jul 13
    1/4 @aran_nayebi and I have created a new mathematical Theory of Contravariance in NeuroAI. There are two high-impact concepts: weak-strong equivalence (WSE) and Zippering (ZIP) that shape how to think about NeuroAI going forward.
    26K
  • user avatar
    Daniel Yamins
    @dyamins
    Apr 25, 2021
    1/ I'm often confronted with skepticism that neural network models of the brain are intelligible, or that they're even proper models at all, considering how "different they look" from real brains.
  • user avatar
    Daniel Yamins
    @dyamins
    Jun 6, 2023
    Counterfactual World Modeling (CWM) is a new project from my group. Our ultimate goal is to build a single unified model that could solve a wide range of human visual tasks in a zero-shot manner -- a kind of pure-vision foundation model. arxiv.org/abs/2306.01828
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    37K
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    Daniel Yamins
    @dyamins
    May 20, 2024
    1/ Our work on unified principles for Topographic Deep Artificial Neural Networks is finally out in Neuron! 7 years in the making.
    cell.com
    A unifying framework for functional organization in early and higher ventral visual cortex
    Margalit et al. develop a topographic artificial neural network that predicts both functional responses and spatial organization of multiple cortical areas of the primate visual system. In turn, the...
    32K
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    Daniel Yamins
    @dyamins
    Jun 18, 2020
    1/7 A big problem with deepnet models of the brain is that they require training on huge supervised datasets. So even if they are approximations of neural responses in the "adult animal", the training process is a totally implausible model of learning in real visual development.
  • user avatar
    Daniel Yamins
    @dyamins
    Mar 25, 2025
    We just finished up Winter quarter CS375: Large-Scale Neural Network Models for Neuroscience. Check out the publicly available Syllabus and lecture notes cs375.stanford.edu/course-calenda…
    23K
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    Daniel Yamins
    @dyamins
    Nov 12, 2023
    If you're interested in computational cognitive neuroscience (CCN) PhD programs, definitely apply to our new track CCN at Stanford CS. cs.stanford.edu/people/faculty… Deadline Dec 5.
    cs.stanford.edu
    Computational Cognitive & Neuro-science
    32K
  • user avatar
    Daniel Yamins
    @dyamins
    Nov 4, 2022
    1/ Do unsupervised learning algorithms match the details of human learning? In our new NeurIPS paper, @ChengxuZhuang and team evaluated this at both real-time and lifelong timescales. Link: openreview.net/forum?id=c0l2Y…
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    Daniel Yamins
    @dyamins
    Nov 9, 2022
    Cool news about the Stanford CS PhD program: We've just added two new interest areas to the application: Computational Cognition & Neuroscience, and Human-Centered AI. Really excited to see this happen - consider applying! cs.stanford.edu/admissions/phd… R . .
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    Daniel Yamins
    @dyamins
    Mar 27, 2025
    New paper on self-supervised optical flow and occlusion estimation from video foundation models. @sstj389 @jiajunwu_cs @SeKim1112 @Rahul_Venkatesh tinyurl.com/dpa3auzd @
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    Daniel Yamins
    @dyamins
    Aug 18, 2022
    1/ Excited to announce EISEN, our new work on self-supervised, category-agnostic instance segmentation: EISEN learns to segment real-world objects from a single image by observing how they move in training videos. neuroailab.github.io/eisen/.
  • user avatar
    Daniel Yamins
    @dyamins
    Jul 10, 2024
    1/ I've long been interested in intrinsic motivation and theory of mind. with @nickhaber and associates, we did a bunch of work over the years. [like arxiv.org/abs/2305.13452 & bit.ly/3W2feXp & arxiv.org/abs/2305.13396] @JulianDeFreitas @_kunoai
    arXiv logo
    arxiv.org
    Measuring and Modeling Physical Intrinsic Motivation
    Humans are interactive agents driven to seek out situations with interesting physical dynamics. Here we formalize the functional form of physical intrinsic motivation. We first collect ratings of...
    16K
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    Daniel Yamins
    @dyamins
    Oct 28, 2020
    1/ Def'n: a "learning rule" is a functional that converts error signals (for some given objective function) to changes in system parameters (e.g. synaptic strengths) such that error decreases after iterated application.
  • user avatar
    Daniel Yamins
    @dyamins
    Apr 25, 2021
    Replying to @dyamins
    13/ We thus argue for a "contravariance" principle: the harder the constraint, the smaller the set of mechanisms that can solve the constraint, and thus the more likely any two solving mechanisms (whether biological or artificial) are to be similar in key ways.
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