Delta Embedding Learning
Learning from corpus and learning from supervised NLP tasks both give useful semantics that can be incorporated into a good word representation. We propose an embedding learning method called Delta Embedding Learning, to learn semantic information from high-level supervised tasks like reading comprehension, and combine it with an unsupervised word embedding. The simple technique not only improved the performance of various supervised NLP tasks, but also simultaneously learns improved universal word embeddings out of these tasks. …

Association for Uncertainty in Artificial Intelligence (AUAI)
The Association for Uncertainty in Artificial Intelligence is a non-profit organization focused on organizing the annual Conference on Uncertainty in Artificial Intelligence (UAI) and, more generally, on promoting research in pursuit of advances in knowledge representation, learning and reasoning under uncertainty. Principles and applications developed within the UAI community have been at the forefront of research in Artificial Intelligence. The UAI community and annual meeting have been primary sources of advances in graphical models for representing and reasoning with uncertainty. …

Deep Product Quantization (DPQ)
Despite their widespread adoption, Product Quantization techniques were recently shown to be inferior to other hashing techniques. In this work, we present an improved Deep Product Quantization (DPQ) technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. A novel loss function, Joint Central Loss, is introduced, which both improves the retrieval performance, and decreases the discrepancy between the soft and the hard representations. Finally, by using a normalization technique, we improve the results for cross-domain category retrieval. …

Data Blending
Data blending is the process of combining data from multiple sources to reveal deeper intelligence that drives better business decision-making. Data blending differs from data integration and data warehousing in that its primary use is not to create the single, unified version of the truth that is stored in systems of record. Rather, business and data analysts use data blending to build an analytic dataset to assist in answering a specific business questions and driving a particular business process. …