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Learn how PyTorch can extend language modeling beyond classification to practical time-to-event prediction. Witold Czubala of UBS will share a PyTorch-based transformer model that predicts six-month attrition from longitudinal advisor–client email communications for wealth management during PyTorch Conference North America in San Jose, CA. Join us October 20-21: #PyTorchCon
17,124 görüntüleme • 1 ay önce •via X (Twitter)
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PyTorch1 ay önce
From Client Communications to Competing Risks: A PyTorch Transformer for Client Attrition Model by @witty_smart

kettybluce1 ay önce
@UBS Interesting bridge between language modeling and survival analysis—time-to-event targets make the model useful for decisions, not just classification benchmarks.

kettybluce1 ay önce
@UBS A PyTorch transformer predicting six-month attrition from longitudinal advisor–client emails is a strong reminder that reproducibility needs the exact data framing and evaluation scope, not just a model screenshot.









