Podcast
Journal Club
Hosted by JournalClub.io · EN
The latest academic research, explained in plain English.
15episodes
Episodes
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A relaxation approach to layerwise determination of learning rates in deep neural networks
2w ago00:13:20Tap 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
2w ago00:11:55Tap 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
3w ago00:15:02Tap 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
3w ago00:10:23Tap 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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Group-Based Recommendation System Using Bi-Stage Adaptive Deep Learning Model
May 800:16:49Tap to summarizeToday's article comes from the International Journal of Computational Intelligence Systems. The authors are Chilukuri et al., from St. Jude Childrens Cancer Research Hospital, in Tennessee. In this paper they're proposing a two-stage deep learning system for group recommendations.
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Grey Wolf Optimization and Deep Belief Networks for Data-Efficient Forecasting in Smart Renewable Energy Systems
May 800:11:51Tap to summarizeToday's article comes from the Journal of Universal Computer Science. The authors are Altherwi et al., from Jazan University, in Saudi Arabia. In this paper they're combining a Deep Belief Network (DBN) with Grey Wolf Optimization (GWO) to create a pipeline that can better predict the output of Hybrid Renewable Energy Systems (HRES).
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A lightweight approach to software fault localization using static features of statements in cloud computing environments
May 800:11:30Tap to summarizeToday's article comes from the Frontiers in Computer Science journal. The authors are Xiao et al., from Hunan Institute of Engineering, in China. In this paper, they're taking the signals that fault localization normally uses and augmenting them with static features, derived from the repo.
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An Object Detection Algorithm Based on Deep Learning and Salient Feature Fusion for Roadside Surveillance Camera
May 800:14:40Tap to summarizeToday's article comes from the CAAI Transactions on Intelligence Technology journal. The authors are He et al., from Yanshan University, in China. In this paper they're showcasing a new object detector for roadside cameras. Their framework augments a standard YOLO pipeline with a second branch that extracts boundary and texture features, then fuses them with RGB features to better distinguish between objects.
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Shared Autoencoder-Based Unified Intrusion Detection Across Heterogeneous Datasets for Binary and Multi-Class Classification Using a Hybrid CNN–DNN Model
May 800:12:12Tap to summarizeToday's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Kamal et al., from the German University in Cairo (GUC). In this paper they build can intrusion detection system (IDS) that can operate at multiple network-layers at once. How? It uses a shared autoencoder with projection layers that map each level of data into a common latent space.
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Novel Loss Functions for Improved Data Visualization in t-SNE
May 800:12:28Tap to summarizeToday's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Nassar et al., from Hamad Bin Khalifa University, in Qatar. In this paper they're evaluating two replacements for KL-divergence within t-SNE. Max-Flipped KL Divergence (KLmax) and KL-Wasserstein Loss.
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