The results demonstrate that interaction data can be used to infer users' engagement during and after an experience, and the proposed techniques are relevant to better understand audience preference and responses.
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.
Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance.
In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule.
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage.
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors.
Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large.