Shi Gu @ NeurIPS (@gushilab) 's Twitter Profile
Shi Gu @ NeurIPS

@gushilab

Professor @ UESTC
Network Neuroscience & Artificial Intelligence

ID: 1279296515324825601

linkhttp://guslab.org calendar_today04-07-2020 06:10:47

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Shi Gu @ NeurIPS (@gushilab) 's Twitter Profile Photo

How will the multitask and incremental learning help shape the modularized structure of both ANNs and brain networks? Meet me at poster A24 on Tuesday at #CCN2024 CogCompNeuro. Excited to meet old and new friends!!!

How will the multitask and incremental learning help shape the modularized structure of both ANNs and brain networks? Meet me at poster A24 on Tuesday at #CCN2024 <a href="/CogCompNeuro/">CogCompNeuro</a>. Excited to meet old and new friends!!!
Shi Gu @ NeurIPS (@gushilab) 's Twitter Profile Photo

Attending #NeurIPS2024 today. Excited to see old and new friends. Our presentation on spiking neural networks (SNNs) will be on Wed 11 Dec 11 a.m. PST 2 p.m. PST at East Exhibit Hall A-C #2410. Happy to chat on NeuroAI both from neuroscience and AI views.

Attending #NeurIPS2024 today. Excited to see old and new friends. Our presentation on spiking neural networks (SNNs) will be on Wed 11 Dec 11 a.m. PST 2 p.m. PST at East Exhibit Hall A-C #2410. Happy to chat on NeuroAI both from neuroscience and AI views.
Jiao Sun (@sunjiao123sun_) 's Twitter Profile Photo

Mitigating racial bias from LLMs is a lot easier than removing it from humans! Can’t believe this happened at the best AI conference NeurIPS Conference We have ethical reviews for authors, but missed it for invited speakers? 😡

Mitigating racial bias from LLMs is a lot easier than removing it from humans! 

Can’t believe this happened at the best AI conference <a href="/NeurIPSConf/">NeurIPS Conference</a> 

We have ethical reviews for authors, but missed it for invited speakers? 😡
Shi Gu @ NeurIPS (@gushilab) 's Twitter Profile Photo

Although post-hoc and probably inaccurate, we can still have some kind of picture about the embedding and geometric evolution of the loss landscape. For the brain-inspired/neuromorphic learning, can we think similarly beyond "intuition"?