Andreas Berentzen
@andreasberentzen.bsky.social
MS Artificial Intelligence @ UvA | Intern @ TNO | working on anomaly detection in spatio-temporal graph data for critical infrastructure.
Reposted by Andreas Berentzen
Training of a 2 → 100 → 2 → 5 fully connected ReLU neural net via cross-entropy minimisation.
• it starts outputting small embeddings
• around epoch 300 learns an identity function
• takes 1700 epochs more to unwind the data manifold
• it starts outputting small embeddings
• around epoch 300 learns an identity function
• takes 1700 epochs more to unwind the data manifold
April 8, 2025 at 4:19 AM
Training of a 2 → 100 → 2 → 5 fully connected ReLU neural net via cross-entropy minimisation.
• it starts outputting small embeddings
• around epoch 300 learns an identity function
• takes 1700 epochs more to unwind the data manifold
• it starts outputting small embeddings
• around epoch 300 learns an identity function
• takes 1700 epochs more to unwind the data manifold
Reposted by Andreas Berentzen
Tomorrow, thursday, feb 20th, 11am EDT, Andreas Berentzen @andreasberentzen.bsky.social and Jesse Wonnink will present their Reproducibility study of: Explaining Temporal Graph Models Through an Explorer-Navigator Framework (TMLR and NeurIPS Journal Track).
🪩
zoom link on website
🪩
zoom link on website
February 19, 2025 at 8:54 AM
Tomorrow, thursday, feb 20th, 11am EDT, Andreas Berentzen @andreasberentzen.bsky.social and Jesse Wonnink will present their Reproducibility study of: Explaining Temporal Graph Models Through an Explorer-Navigator Framework (TMLR and NeurIPS Journal Track).
🪩
zoom link on website
🪩
zoom link on website