8 citations · 8 across the 1 of their papers we have counts for
4 papers
IBMA: An Imputation-Based Mixup Augmentation Using Self-Supervised Learning for Time Series Data
Dang Nha Nguyen, Hai Dang Nguyen, Khoa Tho Anh Nguyen
Data augmentation in time series forecasting plays a crucial role in enhancing model performance by introducing variability while maintaining the underlying temporal patterns. Howe…
Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization
Dang Nguyen, Paymon Haddad, Eric Gan +1
Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution da…
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Dang Minh Nguyen, Chenfei Wang, Yan Shen +1
Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may…
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
Yihao Xue, Siddharth Joshi, Dang Nguyen +1
Recently, multimodal contrastive learning (MMCL) approaches, such as CLIP, have achieved a remarkable success in learning representations that are robust against distribution shift…