arXiv research

Improved robustness of 1D CNNs for heart arrhythmia classification.

We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of random observed and unobserved pairs, with a number of samples that grows quadratically with the corpus size, making it expensive to scale to…

Paper develops an online regularization framework for RL to decorrelate features.

New method differentiates square-root Kalman filters robustly.

Quantum-enhanced method improves stock return prediction accuracy.

Proposes an alternative invariance penalty to address domain generalization issues.

Paper proposes a new method for learning business process representations.

This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into r…

Deep-Gap predicts crowdsourcing supply-demand gaps using deep learning.

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …

The study sets limits on how well systems can be controlled adaptively.

Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time serie…

Reinforcement learning can interact with the environment and is suitable for applications in decision control systems. Therefore, we used the reinforcement learning method to establish a foreign exchange transaction, avoiding the long-standing problem of unstable trends in deep learning predictions. In the system desig…

Unified framework for hyperbolic embeddings from mixed data types.

Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algori…

The paper explores when linear system identification is hard or easy, especially for under-actuated systems.

A new metric for uncertainty quantification using class collisions.

Study forecasts U.S. bond index using deep learning, finding persistence is key.

New measure EC assesses node contributions in nonlinear, time-varying systems.

Study uses Bayes Hilbert framework to recover probability measure flows from sensors.

Deep learning transforms time series into images for anomaly detection in industrial assets.