parnoux

6 ms·

1. 1.▲
   Why people are not in love with Active Learning, yet(medium.com)

   5 points by parnoux 3y ago|0 comments
2. 2.▲
   by parnoux 3y ago

   Interesting that a simple model and hand crafted features would perform this well
3. 3.▲
   Show HN: We made a GPT bot to explain you CV data(loom.com)

   5 points by parnoux 3y ago|0 comments
4. 4.▲
   by parnoux 4y ago

   Yes, that's correct. We integrate with the major ML frameworks to monitor serving data and compare it to the training data to identify potential error patterns and mine the live stream for data to fix them. I'd love to show you th
5. 5.▲
   by parnoux 4y ago

   Thanks for sharing
6. 6.▲
   by parnoux 4y ago

   Thanks for the question and the papers. Like some of those companies, we are believers in the data centric approach to ML. But labeling is not our focus (unlike Snorkel, HumanLoop, Prodigy or Cord.Tech). We focus on model diagnosis and mini
7. 7.▲
   by parnoux 4y ago

   In the example shown in the product tour, we use an approach based on Diversity Sampling. Basically we look for the datapoints that would be the most representative of the drifted domain (and therefore be outliers to the training domain). H
8. 8.▲
   by parnoux 4y ago

   I don't know how tight your legal restrictions are but we work from metadata only. We don't need your text / img / audio. We just need their embeddings and a few other stuff. And we are working on a self hosted version a
9. 9.▲
   by parnoux 4y ago

   I don't think our assumptions are so far appart. The methods you mentioned made it from research to the open source community fairly quickly. In fact, most companies rely on this kind of open research to develop their models. In a lot
10. 10.▲
    by parnoux 4y ago

    You are right. Being able to learn good feature representations through SSL is very powerful. We leverage such representation to perform tasks like semantic search to tackle problems like long tail sampling. We have seen pretty good results