ADVANCES IN IMPROVING SCALABILITY AND ACCURACY OF MLNS USING SYMMETRIES

ADVANCES IN IMPROVING SCALABILITY AND ACCURACY OF MLNS USING SYMMETRIE

Explanation Techniques using Markov Logic Networks

Explanation Techniques using Markov Logic Network

Coarse-to-Fine Lifted MAP Inference in Computer Vision

There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.Comment: Published in IJCAI 201

Online Collective Demand Forecasting for Bike Sharing Services

We introduce a general time-series forecasting method that extends classical seasonal autoregressive models to incorporate exogenous and relational information in an online setting. Our approach is implemented using the probabilistic programming language Probabilistic Soft Logic (PSL). We leverage recent work that enables the scalable application of PSL to online problems and propose novel modeling patterns to leverage dependencies between multiple time series. We demonstrate the applicability and performance of our method for the task of station-level demand forecasting on three bike sharing systems. We perform an analysis of the demand time series and present evidence of relational dependencies among the stations, motivating the need for a forecasting model that leverages the rich relational structure in the bike sharing networks. Our approach significantly improves multi-step forecasting accuracy of autoregressive time-series models on all three datasets. Further, our approach is easily extendable and we expect applicable to a variety of other time-series forecasting problems