1.2k citations · 3.3k across the 19 of their papers we have counts for
20 papers
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…
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
Sang Michael Xie, Hieu Pham, Xuanyi Dong +7
The mixture proportions of pretraining data domains (e.g., Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting…
Symbolic Discovery of Optimization Algorithms
Xiangning Chen, Chen Liang, Da Huang +9
We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient sea…
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Yingtian Zou, Vikas Verma, Sarthak Mittal +6
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.…
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training
Quan Nguyen, Hieu H. Pham, Kok-Seng Wong +3
We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL app…
Enhancing Few-shot Image Classification with Cosine Transformer
Quang-Huy Nguyen, Cuong Q. Nguyen, Dung D. Le +1
This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support sample…