no code implementations • 26 Oct 2023 • Benjamin Yan, Ruochen Liu, David E. Kuo, Subathra Adithan, Eduardo Pontes Reis, Stephen Kwak, Vasantha Kumar Venugopal, Chloe P. O'Connell, Agustina Saenz, Pranav Rajpurkar, Michael Moor
First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist.
1 code implementation • 27 Jul 2023 • Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga, Cyril Zakka, Yash Dalmia, Eduardo Pontes Reis, Pranav Rajpurkar, Jure Leskovec
However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time.
no code implementations • 1 Mar 2023 • Cyril Zakka, Akash Chaurasia, Rohan Shad, Alex R. Dalal, Jennifer L. Kim, Michael Moor, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, Curt Langlotz, Joanna Nelson, William Hiesinger
Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering.
1 code implementation • NeurIPS 2023 • Hamed Nilforoshan, Michael Moor, Yusuf Roohani, Yining Chen, Anja Šurina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec
There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.
no code implementations • 12 Jul 2021 • Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt
Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.
1 code implementation • ICLR 2022 • Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.
1 code implementation • NeurIPS Workshop TDA_and_Beyond 2020 • Michael Moor, Max Horn, Karsten Borgwardt, Bastian Rieck
Topological autoencoders (TopoAE) have demonstrated their capabilities for performing dimensionality reduction while at the same time preserving topological information of the input space.
1 code implementation • 2 Jul 2020 • Zhiliang Wu, Yinchong Yang, Yunpu Ma, Yushan Liu, Rui Zhao, Michael Moor, Volker Tresp
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
2 code implementations • 25 May 2020 • Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.
2 code implementations • ICML 2020 • Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.
2 code implementations • ICML 2020 • Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt
We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders.
no code implementations • 16 Apr 2019 • Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
2 code implementations • 5 Feb 2019 • Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt
This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.
2 code implementations • ICLR 2019 • Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt
While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.