Borja Requena

TL;DR: The Anomalous Diffusion Challenge (AnDi) as mentioned in this paper was an open competition for the characterization of anomalous diffusion from the measurement of an individual trajectory, which traditionally relies on calculating the trajectory mean squared displacement.

TL;DR: This paper presents a meta-anatomy of the response of the immune system to chemotherapy, a model derived from the model developed by Carl Friedrich Gauss in 1916.

TL;DR: This work deliberately coarse-grain a new clickstream proprietary dataset to produce symbolic trajectories with minimal information, and tackles the problem of trajectory classification of arbitrary length and ultimately, early prediction of limited-length trajectories, both for balanced and unbalanced datasets.

TL;DR: This lecture notes provides a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences by covering the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization.

TL;DR: In this paper, a novel approach combining the power of relaxation techniques with deep reinforcement learning is proposed to find the best possible bounds within a limited computational budget, which can be used to solve the ground state energy of many-body quantum systems.