Ordinal regression attempts to solve classification problems in which categories are not independent, but rather follow a natural order. It is crucial to classify each class correctly while learning adequate interclass ordinal relationships. We present a simple and effective method that constrains these relationships among categories by seamlessly incorporating metric penalties into ground truth label representations. This encoding allows deep neural networks to automatically learn intraclass and interclass relationships without any explicit modification of the network architecture. Our method converts data labels into soft probability distributions that pair well with common categorical loss functions such as cross-entropy. We show that this approach is effective by using off-the-shelf classification and segmentation networks in four wildly different scenarios: image quality ranking, age estimation, horizon line regression, and monocular depth estimation. We demonstrate that our general-purpose method is very competitive with respect to specialized approaches, and adapts well to a variety of different network architectures and metrics.

SPIDER, developed at AT&T Labs-Research, is a system that efficiently supports flexible string matching against attribute values in large databases, and is extensively used in AT&T. The scoring methodology is based on tf.idf weighting and cosine similarity, and SPIDER maintains indexes containing string tokens and their weights, for fast matching at query time. Given the "global" nature of the weights maintained in the indexes, even a few updates to the underlying database tables would necessitate a (near-complete recomputation of the indexes, which can be prohibitively expensive. In this paper, we explore novel techniques to considerably reduce the cost of propagating updates in SPIDER, without a significant degradation of answer accuracy or query performance. We present experimental evidence using real data sets to demonstrate the practical benefits of our techniques.

At AT&T Labs-Research, we have been developing a prototype system called SPIDER to efficiently support flexible string matching of attribute values in large databases. SPIDER has been used in AT&T, both as a key component of an operational portal for matching customer names and addresses, and for a variety of ad hoc data quality analyses. In this talk, we report on experiences with SPIDER.

We present a prototype system, SPIDER, developed at AT&T Labs-Research, which supports flexible string attribute value matching in large databases. We discuss the design principles on which SPIDER is based, describe the basic techniques encompassed by the tool and provide a description of the demo.