Dimitris Papailiopoulos of Microsoft Research AI Frontiers limits solver challenge models to 10 million trainable weights
The update bans access to the evaluation test set
@xeophon also 3) can't use frozen models (i thought about a variant of that but it's not very symbolic) where you take a frozen model, and finetune linear probes at the last layer. But that's a different project altogether :p
Dimitris Papailiopoulos@DimitrisPapail@xeophon I will relax them to make it more interesting 1) No more than 10M trainable weights in the solver 2) Can't peak into the test set
@alexjc 1) No more than 10M trainable weights in the solver 2) Can't use frozen models/api calls 3) Can't peak into the test set
Alex J. Champandard 🌱@alexjc@DimitrisPapail If you're willing to include things like units as hard-coded hints you can get more than 15%... what were your rules?
@alexjc i think above 20% is extremely hard!
Alex J. Champandard 🌱@alexjc@DimitrisPapail OK, that's not what I imagined as pure Python program! With trainable weights it's a good approach, but with those rules and a narrow focus I think 50-60% (or more) should be the target? Maybe I should dig out my prototypes to try to add more parameters...
@DimitrisPapail OK, that's not what I imagined as pure Python program! With trainable weights it's a good approach, but with those rules and a narrow focus I think 50-60% (or more) should be the target? Maybe I should dig out my prototypes to try to add more parameters...
Dimitris Papailiopoulos@DimitrisPapail@alexjc 1) No more than 10M trainable weights in the solver 2) Can't use frozen models/api calls 3) Can't peak into the test set