4 citations · 9 across the 5 of their papers we have counts for
5 papers
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting
Hanwei Wu, Ather Gattami, Markus Flierl
We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of availab…
Computing Similarity Queries for Correlated Gaussian Sources
Hanwei Wu, Qiwen Wang, Markus Flierl
Among many current data processing systems, the objectives are often not the reproduction of data, but to compute some answers based on the data resulting from queries. The similar…
Quantization-Based Regularization for Autoencoders
Hanwei Wu, Markus Flierl
Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models a…
Variational Information Bottleneck on Vector Quantized Autoencoders
Hanwei Wu, Markus Flierl
In this paper, we provide an information-theoretic interpretation of the Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss function of the original VQ-VAE ca…
Learning Product Codebooks using Vector Quantized Autoencoders for Image Retrieval
Hanwei Wu, Markus Flierl
Vector-Quantized Variational Autoencoders (VQ-VAE)[1] provide an unsupervised model for learning discrete representations by combining vector quantization and autoencoders. In this…