Qiuling Suo
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View article: External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation Open
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. H…
View article: Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions Open
The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is scarce. Most existing algorithms assume that the underlying …
View article: Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution Open
Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay approach could gradually become less effective for long data…
View article: Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness Open
Recognizing new objects by learning from a few labeled examples in an evolving environment is crucial to obtain excellent generalization ability for real-world machine learning systems. A typical setting across current meta learning algori…
View article: MetaTP
MetaTP Open
With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic…
View article: Metric Learning on Healthcare Data with Incomplete Modalities
Metric Learning on Healthcare Data with Incomplete Modalities Open
Utilizing multiple modalities to learn a good distance metric is of vital importance for various clinical applications. However, it is common that modalities are incomplete for some patients due to various technical and practical reasons i…
View article: Risk Prediction on Electronic Health Records with Prior Medical Knowledge
Risk Prediction on Electronic Health Records with Prior Medical Knowledge Open
Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted considerable attention in recent years, especially with the development of deep learning techniques. Compared with traditional machine learning mo…
View article: Metric Learning from Probabilistic Labels
Metric Learning from Probabilistic Labels Open
Metric learning aims to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. In the traditional settings of metric learning, an implicit assumption is …
View article: Uncorrelated Patient Similarity Learning
Uncorrelated Patient Similarity Learning Open
Patient similarity learning aims to derive a clinically meaningful similarity metric to measure the similarity between a pair of patients according to their historical clinical information, which could help to predict the clinical outcomes…
View article: A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks.
A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks. Open
Monitoring the future health status of patients from the historical Electronic Health Record (EHR) is a core research topic in predictive healthcare. The most important challenges are to model the temporality of sequential EHR data and to …