2 citations · 7 across the 14 of their papers we have counts for
5 papers · 1 filter
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +2
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing. While researchers had attempted to extend t…
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder
Duy A. Nguyen, Trang H. Tran, Huy Hieu Pham +2
In this work, we investigate the time series representation learning problem using self-supervised techniques. Contrastive learning is well-known in this area as it is a powerful m…
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +4
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it…
On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms
Lam M. Nguyen, Trang H. Tran
Stochastic gradient descent (SGD) algorithm is the method of choice in many machine learning tasks thanks to its scalability and efficiency in dealing with large-scale problems. In…
Finite-Sum Optimization: A New Perspective for Convergence to a Global Solution
Lam M. Nguyen, Trang H. Tran, Marten van Dijk
Deep neural networks (DNNs) have shown great success in many machine learning tasks. Their training is challenging since the loss surface of the network architecture is generally n…