Ximeng Mao
@ximengmao.bsky.social
PhD student at Mila – Quebec AI Institute and University of Montreal. Neural-AI / Brain-compute interface.
Transformer is too slow to use?
Come check out our new preprint showing how our proposed POSSM achieves causal prediction in milliseconds, and most importantly, it transfers!
Come check out our new preprint showing how our proposed POSSM achieves causal prediction in milliseconds, and most importantly, it transfers!
New preprint! 🧠🤖
How do we build neural decoders that are:
⚡️ fast enough for real-time use
🎯 accurate across diverse tasks
🌍 generalizable to new sessions, subjects, and even species?
We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes!
🧵1/7
How do we build neural decoders that are:
⚡️ fast enough for real-time use
🎯 accurate across diverse tasks
🌍 generalizable to new sessions, subjects, and even species?
We present POSSM, a hybrid SSM architecture that optimizes for all three of these axes!
🧵1/7
June 6, 2025 at 7:53 PM
Transformer is too slow to use?
Come check out our new preprint showing how our proposed POSSM achieves causal prediction in milliseconds, and most importantly, it transfers!
Come check out our new preprint showing how our proposed POSSM achieves causal prediction in milliseconds, and most importantly, it transfers!
Reposted by Ximeng Mao
Stay tuned for the project page and code, coming soon!
Link: arxiv.org/abs/2506.05320
A big thank you to my co-authors: @nandahkrishna.bsky.social*, @ximengmao.bsky.social*, @mehdiazabou.bsky.social, Eva Dyer, @mattperich.bsky.social, and @glajoie.bsky.social!
🧵7/7
Link: arxiv.org/abs/2506.05320
A big thank you to my co-authors: @nandahkrishna.bsky.social*, @ximengmao.bsky.social*, @mehdiazabou.bsky.social, Eva Dyer, @mattperich.bsky.social, and @glajoie.bsky.social!
🧵7/7
Generalizable, real-time neural decoding with hybrid state-space models
Real-time decoding of neural activity is central to neuroscience and neurotechnology applications, from closed-loop experiments to brain-computer interfaces, where models are subject to strict latency...
arxiv.org
June 6, 2025 at 5:40 PM
Stay tuned for the project page and code, coming soon!
Link: arxiv.org/abs/2506.05320
A big thank you to my co-authors: @nandahkrishna.bsky.social*, @ximengmao.bsky.social*, @mehdiazabou.bsky.social, Eva Dyer, @mattperich.bsky.social, and @glajoie.bsky.social!
🧵7/7
Link: arxiv.org/abs/2506.05320
A big thank you to my co-authors: @nandahkrishna.bsky.social*, @ximengmao.bsky.social*, @mehdiazabou.bsky.social, Eva Dyer, @mattperich.bsky.social, and @glajoie.bsky.social!
🧵7/7
Reposted by Ximeng Mao
Preprint Alert 🚀
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8
June 5, 2025 at 3:32 PM
Preprint Alert 🚀
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8
Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8