In this video, NYU researchers describe their plans to advance deep learning with their new Nvidia DGX-1 AI supercomputer. New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a DGX-1 to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech.

New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a NVIDIA DGX-1 AI supercomputer to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech. These techniques are then applied to computer vision applications like self-driving cars that can understand the environment around them, medical image analysis that can detect tumors or disease earlier and more accurately than traditional methods, and natural language processing that can translate languages, answer questions or hold a dialogue with people.

Wow. Very cool, anyone have any notes/summary?

Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data. It is popular in machine learning and artificial intelligence text books to first consider the learning styles that an algorithm can adopt. There are only a few main learning styles or learning models that an algorithm can have and we'll go through them here with a few examples of algorithms and problem types that they suit. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result. When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods.

Community-based Question Answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for online health communities. In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within online health content that are good features in identifying valid answers. Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. In order to rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. UMLS-based (health-related) features used in the model enhance the algorithm's performance by proximately 8 %. A reasonably high rate of accuracy is obtained given that the data is considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus as well as a number of overlapping health-related terms between questions. Overall, our automated QA system based on historical QA pairs is shown to be effective according to the data set in this case study. It is developed for general use in the health care domain which can also be applied to other CQA sites.

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we propose a new kernel formulation that enables the separation of latent space and derives an efficient variational inference method. The performance of our model is demonstrated with an imbalanced medical image dataset.

We have a set of, say, 10K labeled images (two classes), and an unlabeled set that is maybe 10X larger (or even 100X, doesn't really matter for this discussion). What I'm wondering is can I train a NN on the initial labeled set of 10K images and then use that model to label a larger set of unlabeled images, and then use that larger set of labeled images to train the model again? Will this result in a better model? If so, does anyone have links to literature on this approach and what is called? Is this an example of semi-supervised learning?

In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.