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Found 7 papers, 0 papers with code
Machine Learning for Socially Responsible Portfolio Optimisation
Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return.
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals).
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning
We then investigate the viability of generating “embedding” exemplars from a VAE that can protect base knowledge in the intermediate to output layers of the neural networks.
Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model)
Also, we show that DYnamic COOrdindate Search Using Response Surface Models with XGBoost as a surrogate attains in combination the highest probability of approximating a cumulative synthetic daily infection data distribution and achieves the most significant speedup with regards to our analysis.
Surrogate Assisted Methods for the Parameterisation of Agent-Based Models
We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration.
Unique Faces Recognition in Videos
The contribution of this paper is two-fold: to begin, the experiments have established 3-D Convolutional networks and 2-D LSTMs with the contrastive loss on image sequences do not outperform Google/Inception architecture with contrastive loss in top $n$ rank face retrievals with still images.
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
In this paper, we compare the Gated Recurrent Unit (GRU) and the Long Short-Term Memory (LSTM) network.
