This blog post has recent publications related to Deep Learning for Embedded Systems (e.g. computer systems in toys, biometrics, cars, kitchen equipment, medical equipment such as bionic eyes, etc).
Wikipedia defines Embedded systems as:
An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today. Ninety-eight percent of all microprocessors are manufactured as components of embedded systems
Magnetic Resonance Imaging (MRI) can be used in many types of diagnosis e.g. cancer, alzheimer, cardiac and muscle/skeleton issues. This blog post has recent publications of Deep Learning applied to MRI (health-related) data, e.g. for segmentation, detection, demonising and classification.
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, radio waves, and field gradients to generate images of the organs in the body.
Scaling down images is a craft, scaling up images is an art
Since in the scaling down to a lower resolution you typically need to remove pixels, but in the case of scaling up you need to invent new pixels. But some Deep Learning models with Convolutional Neural Networks (and frequently Deconvolutional layers) has shown successful to scale up images, this is called Image Super-Resolution. These models are typically trained by taking high resolution images and reducing them to lower resolution and then train in the opposite way. Partially related: Recommend also checking out Odeon et. al’s Distill.pub’s publication: Deconvolution and Checkerboard Artifacts that goes into more detail about the one the core operators used in Image Super-Resolution.
This blog post has recent papers about Deep Learning for authentication, e.g. iris (eye), fingerprint and various other patterns of the user, e.g. behavior writing style (stylometry) and other user patterns. Partially related is the Quora question and answer: How can Deep Learning be used for Computer Security?
Tweets (i.e. microblogging with very short documents) is a frequent data source in machine learning, e.g. for sentiment analysis and financial (stock) predictions. Here are some recent papers related to use of Analyzing Twitter Data with Deep Learning. (note: Twitter itself also does Deep Learning on Twitter data with its Cortex Team). Many of these papers could probably also apply similar data sources such as e.g. Weibo or Facebook.
For the last couple of months I’ve been creating bibliographies of recent academic publications in various subfields of Deep Learning on this blog. This posting gives an overview of the last 25 bibliographies posted.
This posting presents recent publications related to Deep Learning for Question Answering. Question Answering is described as “a computer science discipline within the fields of information retrieval and natural language processing (NLP), which is concerned with building systems that automatically answer questions posed by humans in a natural language”. I’ll also publish postings about Deep Learning for Information Retrieval and Learning to Rank today.
Ensemble Based Machine Learning has been used with success in several Kaggle competitions, and this year also the Imagenet competition was dominated by ensembles in Deep Learning, e.g. Trimps-Soushen team from 3rd Research Institute of the Ministry of Public Security (China) used a combination of Inception, Inception-Resnet, Resnet and Wide Residual Network to win the Object Classification/localization challenge. This blog post has recent papers related to Ensembles in Deep Learning.
EEG (Electroencephalography) is the measurement of electrical signals in the brain. It has long been used for medical purposes (e.g. diagnosis of epilepsy), and has in more recent years also been used in Brain Computer Interfaces (BCI) — note: if BCI is new to you don’t get overly excited about it, since these interfaces are still in my opinion quite premature. But they are definitely interesting in a longer term perspective .
This blog post gives an overview of recent research on Deep Learning in combination with EEG, e.g. r for classification, feature representation, diagnosis, safety (cognitive state of drivers) and hybrid methods (Computer Vision or Speech Recognition together with EEG and Deep Learning).
This blog post has recent papers related to embedding for Natural Language Processing with Deep Learning. Example application areas embedding is used for in the papers include finance (stock market prediction), biomedical text analysis, part-of-speech tagging, sentiment analysis, pharmacology (drug adverse effects).
Alzheimer’s Disease is the cause of 60–70% of cases of Dementia, costs associated to diagnosis, treatment and care of patients with it is estimated to be in the range of a hundred billion dollars in USA. This blog post have some recent papers related to using Deep Learning for diagnostics and decision support related to Alzheimer’s disease.
Ultrasound (also called Sonography) are sound waves with higher frequency than humans can hear, they frequently used in medical settings, e.g. for checking that pregnancy is going well with fetal ultrasound. For more about Ultrasound data formats check out Ultrasound Research Interface. This blog post has recent publications about applying Deep Learning for analyzing Ultrasound data.
Deep Learning (creative AI) might potentially be used for music analysis and music creation. Deepmind’s Wavenet is a step in that direction. This blog post presents recent papers in Deep Learning for Music.
This blog post gives an overview of papers related to using Regularization in Deep Learning submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Regularization in Deep Learning check out: www.deeplearningbook.org/contents/regularization.html
This blog post gives an overview of papers related to Unsupervised Deep Learning submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Unsupervised Deep Learning check out: Ruslan Salkhutdinov’s video Foundations of Unsupervised Deep Learning.
This blog post gives an overview of Natural Language Processing related papers submitted to ICLR 2017, see underneath for the list of papers. If you want to learn about Deep Learning with NLP check out Stanford’s CS224d: Deep Learning for Natural Language Processing