Podcast
Journal Club
Hosted by JournalClub.io · EN
The latest academic research, explained in plain English.
21episodes
Episodes
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Towards an MLOps infrastructure for Elastic Pattern experimentation
4w ago00:13:38Tap to summarizeToday's article comes from the SoftwareX journal. The authors are Rodriguez-Cardos et al., from the University of Castilla-La Mancha, in Spain. In this paper, they're showcasing a new experimentation platform for Elastic Patterns research.
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Detecting Uncoded Self-Harm in Veterans’ Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study
Jun 2800:14:12Tap to summarizeToday's article comes from the Journal of Medical Internet Research. The authors are Kumar et al., from the University of New Mexico Health Sciences Center. In this paper they're putting forward a solution for medical "undercoding" that uses a relatively specialized form of machine learning: Positive Unlabeled (PU).
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Transfer learning for T-cell response prediction
Jun 2400:12:34Tap to summarizeToday's article comes from the BMC Bioinformatics journal. The authors are Stadelmaier et al., from the Eberhard Karls University of Tubingen. In this paper the authors build several models for t-cell response prediction. But, they also create an an evaluation scheme that allows them to detect when their models' performance is being inflated by shortcut learning.
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No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection
Jun 2200:17:50Tap to summarizeToday's article comes from the journal of Computer Assisted Surgery. The authors are Connolly et al., from Queen's University in Canada. In this paper they're showcasing an experimental testbed for photoacoustic imaging. It's set up to let researchers prototype, automate, and compare different ways of scanning a tumor bed.
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A relaxation approach to layerwise determination of learning rates in deep neural networks
Jun 1900:13:20Tap to summarizeToday's article comes from the journal Array. The authors are Min et al., from Ewha Womans University, in South Korea. In this paper, they present a framework that helps you choose the right learning rate for every layer in a model.
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Predicting fishing vs. not-fishing in small-scale fisheries: A sample vessel tracking dataset and a reproducible machine learning approach
Jun 1700:11:55Tap to summarizeToday's article comes from the SoftwareX journal. The authors are Lattanzi et al., from the Institute of Marine Biological Resources and Biotechnologies, in Italy. In this paper they're building a binary classifier that can look at the GPS trajectory of a boat and figure out whether its fishing or not.
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Terrestrial Cyborg Insects for Real-Life Applications
Jun 1000:15:02Tap to summarizeToday's article comes from the journal of Advanced Intelligent Systems. The authors are Le et al., from the University of Queensland, in Australia. This paper is an exploration of what's possible when you try to turn insects (like cockroaches, beetles, and grasshoppers), into remote-controlled cyborgs.
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Characterizing wind-dependent low-frequency ambient sound with ocean observatories initiative hydrophones
Jun 900:10:23Tap to summarizeToday's article comes from the JASA Express Letters journal. The authors are Ragland et al., from Woods Hole Oceanographic Institution, in Massachusetts. In this paper, the authors take data collected from a set of OOI's hydrophones, and use it to figure out how much low-frequency underwater-sound is controlled by wind speed.
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Self-adaptive weighting and sampling for physics-informed neural networks
May 2400:13:08Tap to summarizeToday's article comes from the journal of Machine Learning: Science and Technology. The authors are Chen et al., from Pacific Northwest National Laboratory (PNNL), in Washington. In this paper they make the argument that many PINN failures are simultaneously optimization problems and sampling problems. Their solution? Instead of treating adaptive weighting and adaptive sampling as competing alternatives, just combine them into a single unified framework.
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Designing High-Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning
May 2000:11:29Tap to summarizeToday's article comes from the MGE Advances journal. The authors are Nie et al., from Northwestern Polytechnical University, in China. In this paper they're showcasing a pipeline that can hunt for new metals with specific physical properties. It screens through a massive set of candidate compositions, and narrows those down to a handful of promising alloys.
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