Goto

Collaborating Authors

Deep Learning


Assistant/Associate Professor in Dynamics and Deep Learning

#artificialintelligence

Are you a highly motivated researcher with an outstanding track record in Mathematics and its Applications in Science and Engineering? We offer a position at the interface of Dynamics and Deep Learning in the Applied Analysis group of the SACS cluster within the Department of Applied Mathematics (AM) at the University of Twente (UT). The challenge: You will actively develop your mathematical profile and seek connections between fundamental mathematical theory of dynamical systems, nonlinear analysis and the rising area of deep learning for data-driven model discovery. Based on a long-standing expertise and tradition of dynamical systems theory at the UT, well embedded in the Dutch NDNS cluster, our department is looking for a mathematician with a proven expertise in the broad area of dynamical systems, nonlinear analysis or approximation theory for deep neural networks. You show great passion in applying your novel methods to computational neuroscience, inverse problems in imaging or engineering applications driven by physics-informed machine learning for example within the multi-disciplinary research contexts at the UT, like the Digital Society Institute, the Technical Medical Centre or the MESA Institute for Nanotechnology.


Can AI learn to reason about the world like children?

#artificialintelligence

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Even before they speak their first words, human babies develop mental models about objects and people. This is one of the key capabilities that allows us humans to learn to live socially and cooperate (or compete) with each other. But for artificial intelligence, even the most basic behavioral reasoning tasks remain a challenge. Advanced deep learning models can do complicated tasks such as detect people and objects in images, sometimes even better than humans.


Data Science Can Be For Everyone

#artificialintelligence

And this can be tough. You need to be good at statistics, and you need very deep knowledge. And you need to learn Linear Algebra, Calculus as well. However, you need learning machine learning algorithms as well as deep learning algorithms as well. Python and R are also required.


Deep Learning and Glaucoma

#artificialintelligence

Glaucoma is a leading cause of irreversible blindness worldwide. A recent global meta-analysis of 50 population-based studies reported the pooled glaucoma prevalence (age range, 40-80 years) to be 3.5%, corresponding to an estimated 64.3 million individuals worldwide. Li, Zhixi et al. used deep learning system to detect referable GON (glaucomatous optic neuropathy) with high sensitivity and specificity. The study recruited 21 trained ophthalmologists to classify the photographs. Referable GON was defined as vertical cup-to-disc ratio of 0.7 or more and other typical changes of GON.


spaCy Version 3.0 Released: All Features & Specifications

#artificialintelligence

The 3.0 version has state of the art transformer-based pipelines and pre-trained models in seventeen languages. The first version of spaCy was a preliminary version with little support for deep-learning workflows. The second version, however, introduced convoluted neural network models in seven different languages. The third version is a massive improvement over both of these versions. The 3.0 version has completed dropped support for Python 2 and only works on Python 3.6.


Real-World Blind Face Restoration with Generative Facial Prior

#artificialintelligence

Technology and Technological developments in this decade have led to some of the most awe-inspiring discoveries. With rapidly changing technology and systems to support them and provide back-end processing power, the world seems to be becoming a better place to live day by day. Technology has reached such new heights that nothing our ingenious mind today thinks about looks impossible to accomplish. The driving factor of such advancements in this new era of technological and computational superiority seems to be wrapped around two of the most highly debated domains and topics, namely Machine Learning & Artificial Intelligence. The canvas and ideal space that these two domains provide are unfathomable.


DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity - Docwire News

#artificialintelligence

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to "learn" intrinsic patterns in a complex plane of data is the strength of the approach.


Real-time Interpretation: The next frontier in radiology AI - MedCity News

#artificialintelligence

In the nine years since AlexNet spawned the age of deep learning, artificial intelligence (AI) has made significant technological progress in medical imaging, with more than 80 deep-learning algorithms approved by the U.S. FDA since 2012 for clinical applications in image detection and measurement. A 2020 survey found that more than 82% of imaging providers believe AI will improve diagnostic imaging over the next 10 years and the market for AI in medical imaging is expected to grow 10-fold in the same period. Despite this optimistic outlook, AI still falls short of widespread clinical adoption in radiology. A 2020 survey by the American College of Radiology (ACR) revealed that only about a third of radiologists use AI, mostly to enhance image detection and interpretation; of the two thirds who did not use AI, the majority said they saw no benefit to it. In fact, most radiologists would say that AI has not transformed image reading or improved their practices.


Artificial intelligence

#artificialintelligence

The intelligence demonstrated by machines is known as artificial intelligence. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these, machines can be designed to accomplish different tasks in different fields. Email filters, digital calls, data analysis are all examples of NLP. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.


ARTIFICIAL INTELLIGENCE

#artificialintelligence

The intelligence demonstrated by machines is known as artificial intelligence. Artificial intelligence makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using these, machines can be designed to accomplish different tasks in different fields. Email filters, digital calls, data analysis are all examples of NLP. Machines can accurately identify and locate objects then react to what they "see" using digital images from cameras, videos, and deep learning models.