The workflow of data scientists normally involves potentially inefficient processes such as data mining, feature engineering and model selection. Recent research has focused on automating this workflow, partly or in its entirety, to improve productivity. We choose the former approach and in this paper share our experience in designing the client2vec: an internal library to rapidly build baselines for banking applications. Client2vec uses marginalized stacked denoising autoencoders on current account transactions data to create vector embeddings which represent the behaviors of our clients. These representations can then be used in, and optimized against, a variety of tasks such as client segmentation, profiling and targeting. Here we detail how we selected the algorithmic machinery of client2vec and the data it works on and present experimental results on several business cases.

We tackle the detection of prominent objects in images as a retrieval task: given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data driven detection (DDD), that is an alternative to sliding windows. Previous works have used similar notions but with task-independent similarities and representations, i.e. they were not tailored to the end-goal of localization. This article proposes two contributions: (i) a metric learning algorithm and (ii) a representation of images as object probability maps, that are both optimized for detection. We show experimentally that these two contributions are crucial to DDD, do not require costly additional operations, and in some cases yield comparable or better results than state-of-the-art detectors despite conceptual simplicity and increased speed. As an application of prominent object detection, we improve fine-grained categorization by precropping images with the proposed approach.