this site is green baby
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
- learn distances between Markov chains
- extract "encoder-decoder" pairs for representation learning
- with sample- and computational-complexity guarantees
read on for some quick details..
1/n
i likey
i likey
The o1/o3 path to math reasoning is based on LLMs and large-scale test-time search. We argue for a different path that uses formal proof assistants for
✅ creating high-quality synthetic data
✅ rigorous test-time feedback. (1/2)
The o1/o3 path to math reasoning is based on LLMs and large-scale test-time search. We argue for a different path that uses formal proof assistants for
✅ creating high-quality synthetic data
✅ rigorous test-time feedback. (1/2)
Effectively: the default assumption for AMD is that the stack will be broken.
The community tried to help, but as Dave Airlie said years ago “throwing source code over the fence doesn’t make you FOSS”
#HPC #ROCm
arxiv.org/abs/2412.05239
'Conditions for uniform in time convergence: applications to averaging, numerical discretisations, and mean-field systems'
- Katharina Schuh, Iain Souttar
arxiv.org/abs/2412.05239
'Conditions for uniform in time convergence: applications to averaging, numerical discretisations, and mean-field systems'
- Katharina Schuh, Iain Souttar