GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries

Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires employing user information, and conventional generative models, including variational autoencoders (VAEs), often ignore that. This paper introduces GUIDE-VAE, a novel conditional generative model that leverages user embeddings to generate user-guided data. By allowing the model to benefit from shared patterns across users, GUIDE-VAE enhances performance in multi-user settings, even under significant data imbalance. In addition to integrating user information, GUIDE-VAE incorporates a pattern dictionary-based covariance composition (PDCC) to improve the realism of generated samples by capturing complex feature dependencies. While user embeddings drive performance gains, PDCC addresses common issues such as noise and over-smoothing typically seen in VAEs. The proposed GUIDE-VAE was evaluated on a multi-user smart meter dataset characterized by substantial data imbalance across users. Quantitative results show that GUIDE-VAE performs effectively in both synthetic data generation and missing record imputation tasks, while qualitative evaluations reveal that GUIDE-VAE produces more plausible and less noisy data. These results establish GUIDE-VAE as a promising tool for controlled, realistic data generation in multi-user datasets, with potential applications across various domains requiring user-informed modelling.

Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective

Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. The leave-one-out maximum log-likelihood (LOO-MLL) criterion is proposed to prevent the singular solutions that the regular MLL criterion gives rise to, and it is proven that LOO-MLL prevents these. Relying on this guaranteed robustness, the model is extended by assigning learnable weights to the kernels. In addition, a modified expectation-maximization algorithm is employed to accelerate the optimization speed reliably. The performance of the proposed method and models are exhibited on two power systems datasets using different statistical tests and by comparison with Gaussian mixture models. Results show that the proposed models have promising performance, in addition to their singularity prevention guarantees.

Channel State Information Based Localization with Deep Learning

Localization is one of the most important problems in various fields such as robotics and wireless communications. For instance, Unmanned Aerial Vehicles (UAVs) require the information of the position precisely for an adequate control strategy. This problem is handled very efficiently with integrated GPS units for outdoor applications. However, indoor applications require special treatment due to the unavailability of GPS signals. Another aspect of mobile robots such as UAVs is that there is constant wireless communication between the mobile robot and a computational unit. This communication is mainly done for obtaining telemetry information or computation of control actions directly. The responsible integrated units for this transmission are commercial wireless communication chipsets. These units on the receiver side are responsible for getting rid of the diverse effects of the communication channel with various mathematical techniques. These techniques mainly require the Channel State Information (CSI) of the current channel to compensate the channel itself. After the compensation, the chipset has nothing to do with CSI. However, the locations of both the transmitter and receiver have a direct impact on CSI. Even though CSI contains such rich information about the environment, the accessibility of these data is blocked by the commercial wireless chipsets since they are manufactured to provide only the processed information data bits to the user. However, with the IEEE 802.11n standardization, certain chipsets provide access to CSI. Therefore, CSI data became processible and integrable to localization schemes. In this project, a test environment was constructed for the localization task. Two routers with proper chipsets were assigned as transmitter and receiver. They were operationalized for the CSI data collection. Lastly, these data were processed with various deep learning models.