F
Florian Schmidt
Researcher at ETH Zurich
Publications - 12
Citations - 247
Florian Schmidt is an academic researcher from ETH Zurich. The author has contributed to research in topics: Autoregressive model & Generative model. The author has an hindex of 7, co-authored 12 publications receiving 162 citations.
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Neural Document Embeddings for Intensive Care Patient Mortality Prediction
TL;DR: In this paper, a convolutional document embedding approach was proposed for post-discharge mortality prediction using the MIMIC-III intensive care database, which showed significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings.
Proceedings ArticleDOI
Generalization in Generation: A closer look at Exposure Bias
TL;DR: The authors argue that generalization is the underlying property to address exposure bias and propose unconditional generation as its fundamental benchmark, combining latent variable modeling with a recent formulation of exploration in reinforcement learning to obtain a rigorous handling of true and generated contexts.
Proceedings Article
Neural Document Embeddings for Intensive Care Patient Mortality Prediction.
TL;DR: In this paper, a convolutional document embedding approach was proposed for post-discharge mortality prediction using the MIMIC-III intensive care database, which showed significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings.
Proceedings ArticleDOI
How does BERT capture semantics? A closer look at polysemous words.
TL;DR: This rigorous quantitative analysis of linear separability and cluster organization in embedding vectors produced by BERT shows that semantics do not surface as isolated clusters but form seamless structures, tightly coupled with sentiment and syntax.
Posted Content
Generalization in Generation: A closer look at Exposure Bias
TL;DR: It is argued that generalization is the underlying property to address and proposed unconditional generation as its fundamental benchmark in a rigorous handling of true and generated contexts.