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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

PyTorch

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17,124 views • 1 month ago •via X (Twitter)

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3 Comments

PyTorch1 month ago

From Client Communications to Competing Risks: A PyTorch Transformer for Client Attrition Model by @witty_smart

kettybluce1 month ago

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

kettybluce1 month ago

@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.

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