8 citations · 21 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks
Dang Nguyen, Trang Nguyen, Khai Nguyen +3
Layer-wise model fusion via optimal transport, named OTFusion, applies soft neuron association for unifying different pre-trained networks to save computational resources. While en…