Vedant Shah
@veds12.bsky.social
Research at Mila / UdeM
https://veds12.github.io/
https://veds12.github.io/
Reposted by Vedant Shah
(1/n)🚨Train a model solving DFT for any geometry with almost no training data
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...
June 10, 2025 at 7:49 PM
(1/n)🚨Train a model solving DFT for any geometry with almost no training data
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...
Reposted by Vedant Shah
hank you to our funders for this project: CIFAR, NSERC, and Abundant Intelligences. Thank you also for meeting me with the rich discussions @tyrellturing.bsky.social, @veds12.bsky.social, @mnoukhov.bsky.social and @arnaghosh.bsky.social that gave clarity to the problem.
June 5, 2025 at 3:32 PM
hank you to our funders for this project: CIFAR, NSERC, and Abundant Intelligences. Thank you also for meeting me with the rich discussions @tyrellturing.bsky.social, @veds12.bsky.social, @mnoukhov.bsky.social and @arnaghosh.bsky.social that gave clarity to the problem.
Reposted by Vedant Shah
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 5:40 PM
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
I will be presenting our work at the MATH-AI workshop at #NeurIPS2024 today.
Location: West Meeting Room 118-120
Time: 11:00 AM - 12:30 PM; 4:00 PM - 5:00 PM
Come by if you want to chat about designing difficult evaluation benchmarks, follow-up work, and mathematical reasoning in LLMs!
Location: West Meeting Room 118-120
Time: 11:00 AM - 12:30 PM; 4:00 PM - 5:00 PM
Come by if you want to chat about designing difficult evaluation benchmarks, follow-up work, and mathematical reasoning in LLMs!
December 14, 2024 at 4:53 PM
I will be presenting our work at the MATH-AI workshop at #NeurIPS2024 today.
Location: West Meeting Room 118-120
Time: 11:00 AM - 12:30 PM; 4:00 PM - 5:00 PM
Come by if you want to chat about designing difficult evaluation benchmarks, follow-up work, and mathematical reasoning in LLMs!
Location: West Meeting Room 118-120
Time: 11:00 AM - 12:30 PM; 4:00 PM - 5:00 PM
Come by if you want to chat about designing difficult evaluation benchmarks, follow-up work, and mathematical reasoning in LLMs!
I will be at #NeurIPS2024 this week and will be presenting our work
"AI-Assisted Generation of Difficult Math Questions"
at the MATH-AI Workshop on Saturday 🚀!
Would love to chat if you are interested in topics related to LLM reasoning and systematic generalization!
arxiv.org/abs/2407.21009
"AI-Assisted Generation of Difficult Math Questions"
at the MATH-AI Workshop on Saturday 🚀!
Would love to chat if you are interested in topics related to LLM reasoning and systematic generalization!
arxiv.org/abs/2407.21009
December 9, 2024 at 10:45 PM
I will be at #NeurIPS2024 this week and will be presenting our work
"AI-Assisted Generation of Difficult Math Questions"
at the MATH-AI Workshop on Saturday 🚀!
Would love to chat if you are interested in topics related to LLM reasoning and systematic generalization!
arxiv.org/abs/2407.21009
"AI-Assisted Generation of Difficult Math Questions"
at the MATH-AI Workshop on Saturday 🚀!
Would love to chat if you are interested in topics related to LLM reasoning and systematic generalization!
arxiv.org/abs/2407.21009
Reposted by Vedant Shah
Re: the scale is dead debate. Isn't it pretty obvious that just scaling is never going to work if your method breaks down on OOD inputs? The world is non-stationary, so it's constantly presenting new OOD inputs.
November 20, 2024 at 4:55 PM
Re: the scale is dead debate. Isn't it pretty obvious that just scaling is never going to work if your method breaks down on OOD inputs? The world is non-stationary, so it's constantly presenting new OOD inputs.