Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise between fairness and accuracy is desired… Another common approach is to limit the ability of a particular adversary who seeks to maximize parity. Unfortunately, representations produced by adversarial approaches may still retain […]

In the last years, a big interest of both the scientific community and the market has been devoted to the design of audio surveillance systems, able to analyse the audio stream and to identify events of interest; this is particularly true in security applications, in which the audio analytics can be profitably used as an alternative to video analytics systems, but also combined with them. Within this context, in this paper we propose a novel recurrent convolutional neural network architecture, […]

A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of a few nodes… The classical label propagation (LP) method and the emerging graph convolutional network (GCN) are two popular semi-supervised solutions to this problem. The LP method is not effective in modeling node attributes and labels jointly or facing a slow convergence rate on large-scale graphs. GraphHop is proposed to its shortcoming. With proper […]

In this paper we outline the development methodology for an automatic dog treat dispenser which combines machine learning and embedded hardware to identify and reward dog behaviors in real-time. Using machine learning techniques for training an image classification model we identify three behaviors of our canine companions: “sit”, “stand”, and “lie down” with up to 92% test accuracy and 39 frames per second… We evaluate a variety of neural network architectures, interpretability methods, model quantization and optimization techniques to develop […]

Few-shot learning aims to train models on a limited number of labeled samples given in a support set in order to generalize to unseen samples from a query set. In the standard setup, the support set contains an equal amount of data points for each class… However, this assumption overlooks many practical considerations arising from the dynamic nature of the real world, such as class-imbalance. In this paper, we present a detailed study of few-shot class-imbalance along three axes: meta-dataset […]