Question Answering (QA) systems are used to provide proper responses to users' questions automatically. Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem. Given a question, the aim of the task is to find the most similar question from a QA knowledge base. In this paper, we propose a Multi-task Sentence Encoding Model (MSEM) for the PI problem, wherein a connected graph is employed to depict the relation between sentences, and a multi-task learning model is applied to address both the sentence matching and sentence intent classification problem. In addition, we implement a general semantic retrieval framework that combines our proposed model and the Approximate Nearest Neighbor (ANN) technology, which enables us to find the most similar question from all available candidates very quickly during online serving. The experiments show the superiority of our proposed method as compared with the existing sentence matching models.

Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and thei r corresponding definitions in unstructured texts. This task can be formalized either as a sentence classification task (i.e., containing term-definition pairs or not) or a sequential labeling task (i.e., identifying the boundaries of the terms a nd definitions). The previous works for DE have only focused on one of the two approaches, failing to model the interdependencies between the two tasks. In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their interdependencies. Our model features deep learning architectu res to exploit the global structures of the input sentences as we ll as the semantic consistencies between the terms and the definitions, thereby improving the quality of the representat ion vectors for DE. Besides the joint inference between sentenc e classification and sequential labeling, the proposed model is fundamentally different from the prior work for DE in that th e prior work has only employed the local structures of the input sentences (i.e., word-to-word relations), and not yet c on-sidered the semantic consistencies between terms and definitions. In order to implement these novel ideas, our model presents a multi-task learning framework that employs grap h convolutional neural networks and predicts the dependency paths between the terms and the definitions. We also seek to enforce the consistency between the representations of t he terms and definitions both globally (i.e., increasing seman - tic consistency between the representations of the entire s en-tences and the terms/definitions) and locally (i.e., promot ing the similarity between the representations of the terms and the definitions). The extensive experiments on three benchmark datasets demonstrate the effectiveness of our approach.

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.

I hope your week went great! Here's new Python news digest. You can learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep Learning. Python may be also used for things like that!

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.

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.

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.

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:

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.

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.