Geeling Chau
@geelingc.bsky.social
Computation and Neural Systems PhD student @Caltech | Brain Computer Interfaces, Neural Signal Decoding | glchau.github.io
If you missed us, we'll be speaking at 4:06pm (Friday) in Garnet 216-218! Hope to see you there! :)
April 25, 2025 at 7:37 AM
If you missed us, we'll be speaking at 4:06pm (Friday) in Garnet 216-218! Hope to see you there! :)
Would like to especially shout out
yisongyue.bsky.social, Sabera Talukder, Andrei Barbu, and Boris Katz for their valuable advice and unwavering support.
vsub851.bsky.social and Saraswati for their indispensable help. And
czlwang.bsky.social for marching through this with me. 🤗🎉
yisongyue.bsky.social, Sabera Talukder, Andrei Barbu, and Boris Katz for their valuable advice and unwavering support.
vsub851.bsky.social and Saraswati for their indispensable help. And
czlwang.bsky.social for marching through this with me. 🤗🎉
czlwang.bsky.social
czlwang.bsky.social
February 11, 2025 at 6:43 PM
Would like to especially shout out
yisongyue.bsky.social, Sabera Talukder, Andrei Barbu, and Boris Katz for their valuable advice and unwavering support.
vsub851.bsky.social and Saraswati for their indispensable help. And
czlwang.bsky.social for marching through this with me. 🤗🎉
yisongyue.bsky.social, Sabera Talukder, Andrei Barbu, and Boris Katz for their valuable advice and unwavering support.
vsub851.bsky.social and Saraswati for their indispensable help. And
czlwang.bsky.social for marching through this with me. 🤗🎉
I will touch on some work we've done on pretraining transformers across variable input layouts for 🔥 decoding and interpretability.
Read more here: arxiv.org/abs/2406.03044
Read more here: arxiv.org/abs/2406.03044
Population Transformer: Learning Population-level Representations of Neural Activity
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address two key challenges in scaling models with neural time-series...
arxiv.org
December 4, 2024 at 6:52 PM
I will touch on some work we've done on pretraining transformers across variable input layouts for 🔥 decoding and interpretability.
Read more here: arxiv.org/abs/2406.03044
Read more here: arxiv.org/abs/2406.03044