116 citations · 649 across the 37 of their papers we have counts for
10 papers · 1 filter
Exploring galaxy evolution with generative models
Kevin Schawinski, M. Dennis Turp, Ce Zhang
Context. Generative models open up the possibility to interrogate scientific data in a more data-driven way. Aims: We propose a method that uses generative models to explore hypoth…
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli +5
Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guar…
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
Ivan Girardi, Pengfei Ji, An-phi Nguyen +5
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issu…
Using transfer learning to detect galaxy mergers
Sandro Ackermann, Kevin Schawinski, Ce Zhang +2
We investigate the use of deep convolutional neural networks (deep CNNs) for automatic visual detection of galaxy mergers. Moreover, we investigate the use of transfer learning in…
D: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan +2
While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from differ…
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang
Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the…