Collaborating Authors

Connectionist Recommendation in the Wild Artificial Intelligence

In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding. A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them. We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified. From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a field study leading to the ultimate deployment of the system at a university.

Natural Neural Networks

Neural Information Processing Systems

We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning of the Fisher matrix. In particular, we show a specific example that employs a simple and efficient reparametrization of the neural network weights by implicitly whitening the representation obtained at each layer, while preserving the feed-forward computation of the network. Such networks can be trained efficiently via the proposed Projected Natural Gradient Descent algorithm (PRONG), which amortizes the cost of these reparametrizations over many parameter updates and is closely related to the Mirror Descent online learning algorithm. We highlight the benefits of our method on both unsupervised and supervised learning tasks, and showcase its scalability by training on the large-scale ImageNet Challenge dataset.


AAAI Conferences

Due to popularity in texting and messaging, a recent advancement of deep learning technologies, a conversation-based interaction becomes an emerging user interface. While today's conversation platforms offer basic conversation capabilities such as natural language understanding, entity extraction and simple dialogue management, there are still challenges in developing practical applications to support complex use cases using a dialogue system. In this paper, we highlight such challenges and share practical knowledge learned from our experiences on developing a leisure travel shopping application that combines a personalized recommendation system and a conversation system. Such efforts include a conversation design, extraction of user intents, communication of variables between a dialogue system and analytics engines, and dynamic user interface designs. In particular, we introduce our approach to overcome the unique challenges, understanding user's intent, when dialogue system met personalized recommendation system. Furthermore, we propose a semantic mapping as a novel method to utilize undefined user's preferences when producing recommended items. Finally, examples of recommendations based on natural language conversations are provided in order to exhibit how components in the overall architecture are seamlessly orchestrated. In general, our framework provides guiding principles and best practices on the implementation of task-oriented dialogue system connected with other components in the overall architecture.

MOOCs Meet Measurement Theory: A Topic-Modelling Approach

AAAI Conferences

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.

Newbie Seeks Advice


I'm considering a career change into data science, and wondering what you think would be the best way to break in. My main question for you is, what are your recommendations for getting started, and are there any particular educational programs you feel are best? I have been researching online learning programs and plan to start some basic programming modules to test my aptitude for it. I learned a tiny bit of SQL last year while working as a genetic curator with an incredible development team to build a world-class genomic database, which helped open my eyes to the growing and fascinating world of data science. I've looked at Udemy, Udacity, Khan Academy, Coursera, and EdX for fundamental courses - do you have any preferences?