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Found 7 papers, 4 papers with code
Using Interaction Data to Predict Engagement with Interactive Media
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.
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
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.
Augmenting transferred representations for stock classification
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.
Evaluating data augmentation for financial time series classification
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.
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
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.
Breaking the Activation Function Bottleneck through Adaptive Parameterization
Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large.
