Chaudhary, Yatin, Schütze, Hinrich, Gupta, Pankaj

Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics. While introducing topical semantics in language models, existing approaches incorporate latent document topic proportions and ignore topical discourse in sentences of the document. This work extends the line of research by additionally introducing an explainable topic representation in language understanding, obtained from a set of key terms correspondingly for each latent topic of the proportion. Moreover, we retain sentence-topic associations along with document-topic association by modeling topical discourse for every sentence in the document. We present a novel neural composite language model that exploits both the latent and explainable topics along with topical discourse at sentence-level in a joint learning framework of topic and language models. Experiments over a range of tasks such as language modeling, word sense disambiguation, document classification, retrieval and text generation demonstrate ability of the proposed model in improving language understanding.

GANs–Generative adversarial networks (GANs) are deep neural network architectures comprised of two nets pitting one against the other, e.g. the term "adversarial"). The theory of GANs was first introduced in a 2014 paper by deep learning luminary Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio. The potential of GANs is significant because they are generative models in that they create new data instances that resemble training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person.

Binary Tree – a tree data structure where each node has at most two nodes (left and right nodes) and a data element. The topmost node of the tree is the root node. Bayes' Theorem – named after 18th century British mathematician Thomas Bayes, it is a formula for determining conditional probability Eigenvalue – any number such that a given matrix minus that number times the identity matrix has zero determinant. Eigenvector - a vector which when operated on by a given operator gives a scalar multiple of itself. Fourier transform – named after French mathematician Joseph Fourier, it's a method for converting a time function into one expressed in terms of frequency Copyright 2019 Cami Rosso All rights reserved.

Machine learning as a whole is changing the way that we are assessing various algorithmic approaches for problem-solving in our world. Many developers are using this concept to generate improvements with complex decisions and tasks worldwide. Machine learning does represent the future in algorithmic approaches, and it's a model that can help us to the advanced technology of a whole. If you're interested in getting into machine learning, it's very important that you understand some of the basic concepts involved with the machine learning process and development in machine learning. This term has to do with the varying levels of sensitivity and specificity that is directly represented in the curve with ROC.

Deep learning is a relatively new term, although it has existed prior to the dramatic uptick in online searches of late. Enjoying a surge in research and industry, due mainly to its incredible successes in a number of different areas, deep learning is the process of applying deep neural network technologies - that is, neural network architectures with multiple hidden layers - to solve problems. Deep learning is a process, like data mining, which employs deep neural network architectures, which are particular types of machine learning algorithms. Deep learning has racked up an impressive collection of accomplishments of late. In light of this, it's important to keep a few things in mind, at least in my opinion: As shown in the image above, deep learning is to data mining as (deep) neural networks are to machine learning (process versus architecture).

Machine Learning Key Terms Posted on Monday, June 4th, 2018 at 3:03 pm. Share this: This blog post was authored by Soniya Shah. Machine learning seems to be everywhere these days – in the online recommendations you get on Netflix, the self-driving cars that hyped in the media, and in serious cases, like fraud detection. Data is a huge part of machine learning, and so are the key terms. Unless you have a background in statistics or data science, it can be confusing to keep all the terminology straight.

Genetic algorithms, inspired by natural selection, are a commonly used approach to approximating solutions to optimization and search problems. Their necessity lies in the fact that there exist problems which are too computationally complex to solve in any acceptable (or determinant) amount of time. Take the well-known travelling salesman problem, for example. As the number of cities involved in the problem grow, the time required for determining a solution quickly becomes unmanageable. Solving the problem for 5 cities, for example, is a trivial task; solving it for 50, on the other hand, would take an amount of time so unreasonable as to never complete.

This post presents a collection of data science related key terms with concise, no-nonsense definitions, organized into 12 distinct topics. Starting with Big Data and progressing through to natural language processing, this definition train has stops at machine learning, databases, Apache Hadoop, and several more. It may take come time, but once you get through the terminology presented herein, you should have a good idea of the key terms of importance in data science. And don't worry if the definitions are too slim for you; links abound for expanded related reading opportunities where appropriate. If somehow you've made it to this website and have not heard the term since it first gained momentum toward becoming a popular term at least a decade and a half ago, I really don't know what to say.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.