45,034 research outputs found
Escaping Saddle Points with Adaptive Gradient Methods
Adaptive methods such as Adam and RMSProp are widely used in deep learning
but are not well understood. In this paper, we seek a crisp, clean and precise
characterization of their behavior in nonconvex settings. To this end, we first
provide a novel view of adaptive methods as preconditioned SGD, where the
preconditioner is estimated in an online manner. By studying the preconditioner
on its own, we elucidate its purpose: it rescales the stochastic gradient noise
to be isotropic near stationary points, which helps escape saddle points.
Furthermore, we show that adaptive methods can efficiently estimate the
aforementioned preconditioner. By gluing together these two components, we
provide the first (to our knowledge) second-order convergence result for any
adaptive method. The key insight from our analysis is that, compared to SGD,
adaptive methods escape saddle points faster, and can converge faster overall
to second-order stationary points.Comment: Update Theorem 4.1 and proof to use martingale concentration bounds,
i.e. matrix Freedma
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
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