Ian Charest @IanCharest
Cognitive Neuroscientist at the U de M. Generally interested in vision, subjective experience and consciousness. Also co-founder of https://t.co/FU5e0EJKkL iancharest.com Montréal, Québec Joined June 2012-
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🚨New pre-print! How do we form concept representations across vision and language? Using similarity judgements of natural scene images & sentence captions, we show that concepts are formed as structured representations reflecting stable, relational properties of the world.🧵
If you want to know more here's a summary post: x.com/AdrienDoerig/s…
🚨 Finally out in Nature Machine Intelligence!! "Visual representations in the human brain are aligned with large language models" nature.com/articles/s4225…
This is perhaps the most important paper I've contributed to so far. Also the one with most ups and downs. Years in the making. Several rounds of review in many outlets. But it's out now. Thanks to @AdrienDoerig who really drove that thing home. Enjoy! nature.com/articles/s4225…
@NicoleCRust @VFascianelli @StefanoFusi2 Here's one more in humans: pnas.org/doi/10.1073/pn…
We are looking for data from the community to help us prepare for #CCN2025! If you have a few minutes to help out, fill out this survey: forms.gle/6q6CA3VPp1f5rT… - due Aug 31 - 2-10 min (depending on how much optional feedback) - Open to *both* #CCN2024 attendees + non-attendees
Food truck will be out again today 11am-3pm! #CCN2024
Looking for lunch options? We have arranged for a food truck "Tandoor & Curry", today (Tue Aug 6) and tomorrow (Wed Aug 7) from 11am-3pm near Kresge Auditorium. We've also put together some additional recommendations for local food options here: 2024.ccneuro.org/food/
This is again some extraordinary work from the team. Enjoy.
1/13 Heavily updated preprint! arxiv.org/abs/2209.11737 We show that the contextual information encoded in Large Language Models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. 🧵
1/13 Heavily updated preprint! arxiv.org/abs/2209.11737 We show that the contextual information encoded in Large Language Models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. 🧵
New paper from the lab now out in @PNASNexus : Decoding face recognition abilities in the human brain. With @SoubeyrandF, @IanCharest & al.
1/13 Heavily updated preprint! arxiv.org/abs/2209.11737 We show that the contextual information encoded in Large Language Models (LLMs) is beneficial for modelling the complex visual information extracted by the brain from natural scenes. 🧵
📢Submissions for #CCN2024 are now open at ccneuro.org! 📢 We welcome submissions for 2-page papers (deadline: 12 April) and Generative Adversarial Collaborations (GACs), Keynote+Tutorials, and (new this year!) Community Events (deadline: 5 April).
Interested in joining Mila's research community? Our annual supervision request process for new Mila students is starting this Sunday October 15, 2023. For more information ➡️ pulse.ly/62ldgu92bt
sourcherries.github.io/blog/posts/mas… Want to know why #numpy masked arrays are awesome for #DataScience and research?
We are seeking out ideas for future Generative Adversarial Collaborations at CCN! Which challenges and controversies would you like to see discussed, and who should be involved? Let us know your ideas, even if you are not planning to submit a GAC. app.fillout.com/t/eAAUdbJ3S7us
@TimDalgleish @mrccbu That is devastating. Terrible news. I've had some of the best times at the @mrccbu. Any ways this decision by the MRC could be challenged publicly? A petition? I'm guessing you would get many signatures...
We are excited to announce that submissions for CCN 2023 are now open! The submission deadline for papers, Generative Adversarial Collaborations (GACs), and Keynote&Tutorials will be **31 March 2023** (earlier than previous years!). 2023.ccneuro.org
@CyrilRPernet Thanks for the kind words. I think you will like reading this one. Let's catch up soon !
Check out our new preprint! Amazing effort from all. Enjoy!
Preprint! How does the brain make sense of the world? Using a variety of converging analyses and recurrent DNN modeling of large-scale 7T data, we propose that the visual system computes rich semantic scene descriptions, rather than category labels only.🧵 arxiv.org/abs/2209.11737
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