Learning Interpretable Rules for Scalable Data Representation and Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. A novel design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on ten small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.Comment: Accepted by IEEE TPAMI in October 2023; Interpretable ML; Neuro-Symbolic AI; Preliminary conference version (NeurIPS 2021) available at arXiv:2109.1510

Statistical Vehicle Specific Power Profiling for Urban Freeways

AbstractVehicle Specific Power (VSP) is conventionally defined to represent the instantaneous vehicle engine power. It has been widely utilized to reveal the impact of vehicle operating conditions on emission and energy consumption estimates that are dependent upon speed, roadway grade and acceleration or deceleration on the basis of the second-by-second vehicle operation. VSP has hence been incorporated into a key contributing factor in the vehicle emission models including MOVES. To facilitate the preparation of MOVES vehicle operating mode distribution inputs, an enhanced understanding and modeling of VSP distribution versus roadway grade become indispensable. This paper presents a study in which previous studies are extended by deeply investigating the characteristics of VSP distributions and their impacts due to varying freeway grades, as well as time-of-day factors. Afterwards, statistical distribution models with a scope of bins is identified through a goodness of fit testing approach by using the sample data collected from the interstate freeway segments in Cincinnati area. The Global Positioning System (GPS) data were collected at a selected length of 30km urban freeway for AM, PM and Mid-day periods. The datasets representing the vehicle operating conditions for VSP calculation are then extracted from the GPS trajectory data. The distribution fit modeling results demonstrated that the Wakeby distribution with five parameters dominates the most fitting parameters with the samples. In addition, the speed variation lies behind the time of day differences is also identified to be a contributing factor of urban freeway VSP distribution

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bound- ing boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth- based 3D proposal generation.Chinese Scholarship Council; CSIRO-Data61; The Program of Shanghai Subject Chief Scientist (A type) (No.15XD1502900)

Indoor Scene Parsing with Instance Segmentation, Semantic Labeling and Support Relationship Inference

Over the years, indoor scene parsing has attracted a growing interest in the computer vision community. Existing methods have typically focused on diverse subtasks of this challenging problem. In particular, while some of them aim at segmenting the image into regions, such as object instances, others aim at inferring the semantic labels of given regions, or their support relationships. These different tasks are typically treated as separate ones. However, they bear strong connections: good regions should respect the semantic labels; support can only be defined for meaningful regions; support relationships strongly depend on semantics. In this paper, we, therefore, introduce an approach to jointly segment the object instances and infer their semantic labels and support relationships from a single input image. By exploiting a hierarchical segmentation, we formulate our problem as that of jointly finding the regions in the hierarchy that correspond to instances and estimating their class labels and pairwise support relationships. We express this via a Markov Random Field, which allows us to further encode links between the different types of variables. Inference in this model can be done exactly via integer linear programming, and we learn its parameters in a structural SVM framework. Our experiments on NYUv2 demonstrate the benefits of reasoning jointly about all these subtasks of indoor scene parsing.Chinese Scholarship Council; CSIRO-Data61