OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper, we introduce OpenML-Python, a client API for Python, which opens up the OpenML platform for a wide range of Python-based machine learning tools. It provides easy access to all datasets, tasks and experiments on OpenML from within Python. It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML. Furthermore, it comes with a scikit-learn extension and an extension mechanism to easily integrate other machine learning libraries written in Python into the OpenML ecosystem. Source code and documentation are available at https://github.com/openml/openml-python/.

We present a structured learning approach to semantic annotation of RGB-D images. Our method learns to reason about spatial relations of objects and fuses low-level class predictions to a consistent interpretation of a scene. Our model incorporates color, depth and 3D scene features, on which an energy function is learned to directly optimize object class prediction using the loss-based maximum-margin principle of structural support vector machines. We evaluate our approach on the NYU V2 dataset of indoor scenes, a challenging dataset covering a wide variety of scene layouts and object classes. We hard-code much less information about the scene layout into our model then previous approaches, and instead learn object relations directly from the data. We find that our conditional random field approach improves upon previous work, setting a new state-of-the-art for the dataset.

Unsupervised learning algorithms find ways to model latent structure present in the data. These latent structures can then serve as a basis for supervised classification methods. A common choice for unsupervised feature discovery is the Restricted Boltzmann Machine (RBM). Since the RBM is a general purpose learning machine, it is not particularly tailored for image data. Representations found by RBMs are consequently not image-like. Since it is essential to exploit the known topological structure for image analysis, it is desirable not to discard the topology property when learning new representations. Then, the same learning methods can be applied to the latent representation in a hierarchical manner. In this work, we propose a modification to the learning rule of locally connected RBMs, which ensures that topological image structure is preserved in the latent representation. To this end, we use a Gaussian kernel to transfer topological properties of the image space to the feature space. The learned model is then used as an initialization for a neural network trained to classify the images. We evaluate our approach on the MNIST and Caltech 101 datasets and demonstrate that we are able to learn topological feature maps.