Application of Big Data Analytics for Understanding the Complexity of Vehicle Routing Problems

Application of Big Data Analytics for Understanding the Complexity of Vehicle Routing Problem

Reinforcement Learning and Physics

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.Ministerio de Ciencia e Innovación PGC2018- 095113-B-I00, PID2019-104002GB-C21 and PID2019-104002GB-C2

Unsupervised Cross-Subject BCI Learning and Classification using Riemannian Geometry

International audienceThe inter-subject variability poses a challenge in cross-subject Brain-Computer Interface learning and classification. As a matter of fact, in cross-subject learning not all available subjects may improve the performance on a test subject. In order to address this problem we propose a subject selection algorithm and we investigate the use of this algorithm in the Riemannian geometry classification framework. We demonstrate that this new approach can significantly improve cross-suject learning without the need of any labeled data from test subjects

Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional and Recurrent Neural Networks

Non-intrusive sensor-based human activity recognition is utilized in a spectrum of applications including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs) recurrent neural networks provide a way to achieve human activity recognition accurately and effectively. This project designed and explored a variety of multi-layer hybrid deep learning architectures which aimed to improve human activity recognition performance by integrating local features and was scale invariant with dependencies of activities. We achieved a 94.7% activity recognition rate on the University of California, Irvine public domain dataset for human activity recognition containing 6 activities with a 2-layer CNN-1-layer LSTM hybrid model. Additionally, we achieved an 88.0% activity recognition rate on the University of Texas at Dallas Multimodal Human Activity dataset containing 27 activities with a 4-layer CNN-1-layer LSTM hybrid model. For both datasets, our hybrid models outperformed other deep learning models and traditional machine learning methods

Weightless Neural Networks for text classification using tf-idf

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