Hongyu_Chang
@hongyu_chang
Grad student in @azayhara and @antferrui lab @Cornell NBB, alumnus from Haruo Kasai lab @IRCN_UTokyo, She/her [email protected] http://hongyuchang.bsky.social
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It was again a great pleasure to collaborate with team! Check out our most recent work! 🤔how does brain 🧠 solve time and space in generalization task!
Excited to share our latest story! We found disentangled memory representations in the hippocampus that generalized across time and environments, despite the seemingly random drift and remapping of single cells. This code enabled the transfer of prior knowledge to solve new tasks
🇨🇳 We’re screwed … it becomes indistinguishable from reality Ali’s Wan 2.2 lets you stream without showing your face. It maps your voice and motion onto another face.
Harvard professor literally dropped the best ML systems tutorial you’ll ever see
This is wild... I just built an AI-powered Team Performance Optimizer in minutes with a single AI tool. It tracks team productivity, analyzes collaboration patterns, and gives personalized improvement insights automatically. Here’s how 👇
I can’t stop replaying the #memories from our first-ever New York Memory Hub conference! So grateful to everyone who made it a success and inspired us with their research 🧠
Check out our poster at @SfNtweets annual meeting #SfN2025 - MM3 on Nov 18 Tues PM Our first foray using trans-synaptic rabies virus to study #psychedelic drug action, visualized and quantified via @LifeCanvasTech whole-brain imaging 🧠🔬
We would inject psilocybin or saline, then used monosynaptic rabies viruses to trace the input cells to the frontal cortical pyramidal neurons. The whole-brain images were beautiful! 🔴starter cells. 🟢 input cells. #FluorescenceFriday 3/12
That's a wrap! So many incredible talks at our Memory Hub Conference today! 🧠
🚨BREAKING: MIT researchers discover how to enable LLMs to do real logical reasoning. This is what you need to know: (thread)
"Introduction to Machine Learning Systems" - FREE from MIT Press - Authored by Harvard Professor - 2048 Pages To Get It Simply: 1. Retweet & Reply "ML" 2. Follow so that I will DM you.
However, this doesn’t mean that Western schools of philosophy are of lesser value to me. No, not at all! I have immensely benefitted from them and will cherish them all my life. 😊
The more I study the Upanishads, the more convinced I become that what Immanuel Kant and his ilk have written is merely a small subset of the Upanishads. The first image is from “Critique of Pure Reason,” and for a glimpse into re laity check, read attached “Roots of Vedanta”.
"Tensor Analysis on Manifolds" by Richard L. Bishop and Samuel I. Goldberg.
Stanford's "Mathematical Methods for Computer Vision, Robotics, and Graphics" PDF: graphics.stanford.edu/courses/cs205a… Video Lectures: youtube.com/playlist?list=…
Stanford Deep Learning for Computer Vision taught by Professor Fei-Fei Li (@drfeifei) and Assistant Professor Ehsan Adeli. Such an enjoyable YT series. (link in comment)
Fei-Fei Li (@drfeifei) on limitations of LLMs. "There's no language out there in nature. You don't go out in nature and there's words written in the sky for you.. There is a 3D world that follows laws of physics." Language is purely generated signal.
Curious about how mice follow odor trails? Excited to share our latest (long-time brewing)! We watched mice on an "endless" treadmill. Combining experiments with Bayesian modeling we show that mice use predictive (not reactive) strategies!
Time-compressed theta sequences, representational drift, and flickering between alternate representations are fascinating network phenomena. Now we reveal what they have in common and how and when they emerge and express via generative processes: rdcu.be/eCGWt 1/5
🚨 BREAKING: NVIDIA just exposed the dirty secret about LLMs. Their new paper proves SLMs outperform massive models in real-world applications. AI researchers are quietly pivoting overnight. 10 wild findings that change everything:
Introduction to Machine Learning by J. Deng and R. Fong and V. Ramaswamy Lecture notes: princeton-introml.github.io
This 277-page PDF unlocks the secrets of Large Language Models. Here's what's inside: 🧵
"Calculus on Manifolds" by Michael Spivak.
New preprint from collaboration with @SN_Lab - 2p imaging of psilocybin's effects on neurovascular coupling 🔬 We found that psilocybin prolongs the neurovascular response, independent of neural activity. This affects how we should interpret fMRI BOLD studies of #psychedelics‼️
Excited to share our latest work is up on bioRxiv! Looking at capillary flow speed changes we show that psilocybin prolongs the neurovascular coupling response to a visual stimulus. 🍄🔬🧠🩸 Preprint: biorxiv.org/content/10.110…
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