14 citations · 56 across the 9 of their papers we have counts for
7 papers · 1 filter
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification
Tian Tian, Chunyan Miao, Hangwei Qian
Contrastive learning has emerged as a competent approach for unsupervised representation learning. However, the design of an optimal augmentation strategy, although crucial for con…
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning
Chenyu Sun, Hangwei Qian, Chunyan Miao
Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected dataset. Most existing works are to develop sophisticated learning algorithms, with less…
Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations
Yongjie Wang, Hangwei Qian, Yongjie Liu +2
Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and…
Unsupervised Representation Learning for Time Series: A Review
Qianwen Meng, Hangwei Qian, Yong Liu +3
Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. Enablin…
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Qianwen Meng, Hangwei Qian, Yong Liu +3
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recentl…
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning
Chenyu Sun, Hangwei Qian, Chunyan Miao
In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding…
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?
Hangwei Qian, Tian Tian, Chunyan Miao
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-s…