Neural Networks


Deep Learning in Genomics

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You are invited to attend our event next Monday, Nov 18th @6:00 pm at Venture X. Come and join us as Dr. Huang gives a talk on how Deep Learning is used in Genomics. If you are curious about Artificial Intelligence & Data Science in Genomics and want to learn more, then this talk is for you. Dr. Huang's expertise is in the areas of Computational Biology, Computational Neuroergonomics, Brain-Computer Interface, Statistical Modeling, and Bayesian Methods. Dr. Yufei Huang is a Professor and Associate Chair in Research at the Department of Electrical and Computer Engineering at UTSA. He is also an adjunct professor at the Dept. of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio.


Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

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Machine learning (ML) methods reach ever deeper into quantum chemistry and materials simulation, delivering predictive models of interatomic potential energy surfaces1,2,3,4,5,6, molecular forces7,8, electron densities9, density functionals10, and molecular response properties such as polarisabilities11, and infrared spectra12. Large data sets of molecular properties calculated from quantum chemistry or measured from experiment are equally being used to construct predictive models to explore the vast chemical compound space13,14,15,16,17 to find new sustainable catalyst materials18, and to design new synthetic pathways19. Recent research has explored the potential role of machine learning in constructing approximate quantum chemical methods20, as well as predicting MP2 and coupled cluster energies from Hartree–Fock orbitals21,22. There have also been approaches that use neural networks as a basis representation of the wavefunction23,24,25. Most existing ML models have in common that they learn from quantum chemistry to describe molecular properties as scalar, vector, or tensor fields26,27.


How we can use Deep Learning with Small Data? – Thought Leaders

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When it comes to keeping up with emerging cybersecurity trends, the process of staying on top of any recent developments can get quite tedious since there's a lot of news to keep up with. These days, however, the situation has changed dramatically, since the cybersecurity realms seem to be revolving around two words- deep learning. Although we were initially taken aback by the massive coverage that deep learning was receiving, it quickly became apparent that the buzz generated by deep learning was well-earned. In a fashion similar to the human brain, deep learning enables an AI model to achieve highly accurate results, by performing tasks directly from the text, images, and audio cues. Up till this point, it was widely believed that deep learning relies on a huge set of data, quite similar to the magnitude of data housed by Silicon Valley giants Google and Facebook to meet the aim of solving the most complicated problems within an organization.


The 8 Minute Guide To How Your Business Can Solve Problems with AI and Machine Learning

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You can categorize visitors to your website into buckets based on their click patterns, gaining insights to what they are likely to buy or be interested in. Given some data about a person, a machine learning algorithm can categorize them as likely having an illness or not. Network traffic can be classified as malicious or not. Email can be classified as spam, or not spam. Machines which are likely to fail soon can be identified and maintenance performed prior to failure, based on vibration, current consumption, or other measurements.


Detection of face Manipulated Videos using Deep Learning

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Faceforensics data was collected by Visual Computing Group which an active research group on computer vision, computer graphics, and machine learning. This data contains 1000 pristine (real) videos that are selectively downloaded from YouTube such that all videos have clear face visibility (videos that are mostly like news-readers reading news). These pristine videos are manipulated by using 3 state-of-art video manipulation techniques such as DeepFakes, FaceSwap, Face2Face.To understand more about the data please refer to this paper. I have downloaded a total of 100 raw videos (49real 51 fake) covering all the categories and these videos are extracted into images. To download and extract the images please go through this Github page and read the instructions carefully. Before building any Machine learning/ Deep learning models we need to understand the data with some Data Analysis. Let's get an idea of how this data is organized:


Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs

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Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning–based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Investigators at four medical centers retrospectively identified 600 lung cancer–containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning–based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software.


Evolving Neural Networks

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In the __init__ function, we set up the network. The parameter dimensions is a list of layer dimensions, where the first is the width of the input, the last is the width of the output, and all others are hidden dimensions. The __init__ function iterates through these n dimensions to create n-1 weight matrices using Glorot Normal initialization, which are stored as layers. If bias is enabled, a non-zero bias vector is also stored for each layer. The model uses ReLU activation for all internal layers.


Geometric deep learning -- Convolutional Neural Networks on Graphs and Manifolds

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The main idea of spectral approaches such as Graph neural networks is to generalize the Fourier transform theorem for graph and manifold data and doing the convolution on the spectral domain. The generalization of the Fourier transform consists on using the already defined eigenfunctions of graph laplacian as bases for the Fourier transform. The process to apply a convolution using this generalization is as follows. This approach has presented very good results on data presented as a graph, but has an important weakness: Laplacian eigenfunctions are inconsistent across different domains.


Compositional Deep Learning

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The inability of Deep Learning to perform compositional learning is one of the main reasons for Deep Learning's most critical limitations, including the need to feed them tons of data. Compositionality is the algebraic capacity to understand and produce novel combinations from known components (Loula 2018). While the human brain can easily learn compositionally, Neural Networks (NNs) are not able to discover and store skills that are common across problems, and to re-combine them in a hierarchical fashion to solve new challenges (Liška 2018). The human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before the human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before (Loula 2018). This principle helps to explain how, when acquiring a language, we can quickly bootstrap to a potentially infinite number of expressions from very limited training data (Loula 2018).


What if I train a deep neural network with random data?

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Would you train a neural network with random data? Moreover, are massive neural networks just lookup tables or do they truly learn something? Today's episode is about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski. Stan works as post-doc at New York University. I have asked Stan a few questions I was looking answers for a long time.