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Found 8 papers, 4 papers with code
Universal Time-Series Representation Learning: A Survey
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies.
Adaptive Shortcut Debiasing for Online Continual Learning
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment.
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Unlabeled data examples awaiting annotations contain open-set noise inevitably.
Meta-Learning for Online Update of Recommender Systems
It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Time-series data are ubiquitous these days, but lack of the labels in time-series data is regarded as a hurdle for its broad applicability.
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distributed(IID) data and high communication cost.
Robust Learning by Self-Transition for Handling Noisy Labels
In the seeding phase, the network is updated using all the samples to collect a seed of clean samples.
Learning from Noisy Labels with Deep Neural Networks: A Survey
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data.
