We propose a new method, named isolation Mon- drian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.
As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets.
Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative environment. In this work, we present techniques for centralized training of Multi-Agent Deep Reinforcement Learning (MARL) using the model-free Deep Q-Network (DQN) as the baseline model and communication between agents. We present two novel, scalable and centralized MARL training techniques (MA-MeSN, MA- BoN), which achieve faster convergence and higher cumulative reward in complex domains like autonomous driving simulators. Subsequently, we present a memory module to achieve a decentralized cooperative pol- icy for execution and thus addressing the challenges of noise and com- munication bottlenecks in real-time communication channels. This work theoretically and empirically compares our centralized and decentralized training algorithms to current research in the field of MARL. We also present and release a new OpenAI-Gym environment which can be used for multi-agent research as it simulates multiple autonomous cars driving on a highway. We compare the performance of our centralized algorithms to existing state-of-the-art algorithms, DIAL and IMS based on cumu- lative reward achieved per episode. MA-MeSN and MA-BoN achieve a cumulative reward of at least 263% of the reward achieved by the DIAL and IMS. We also present an ablation study of the scalability of MA- BoN showing that it has a linear time and space complexity compared to quadratic for DIAL in the number of agents.