Tao Yang

Efficient Measurement of Multiparticle Entanglement with Embedding Quantum Simulator

2016 · Physical Review Letters

The quantum measurement of entanglement is a demanding task in the field of quantum information. Here, we report the direct and scalable measurement of multiparticle entanglement with embedding photonic quantum simulators. In this embedding framework …

On the Generation of Medical Question-Answer Pairs

2018 · arXiv (Cornell University)

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality …

Multiplex Graph Neural Network for Extractive Text Summarization

2021 · arXiv (Cornell University)

Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies …

Reinforcement Learning to Rank Using Coarse-grained Rewards

2022 · arXiv (Cornell University)

Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-level annotations) have achieved significant success, their reliance on costly and …

Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios

2023 · arXiv (Cornell University)

Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams, which holds great value for the deployment of models in real-world applications. Numerous studies have …

Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach

2024

Ranking is at the core of many artificial intelligence (AI) applications, including search engines, recommender systems, etc. Modern ranking systems are often constructed with learning-to-rank (LTR) models built from user behavior signals. While previous studies …

Fed-SMAE: Federated-Learning Based Time Series Anomaly Detection with Shared Memory Augmented Autoencoder

2024

Time series anomaly detection plays a critical role in ensuring the security of Cyber-Physical Systems (CPS). However, the growing complexity of data acquired from CPS poses significant challenges to conventional anomaly detection methods. Deep learning-based …

AdaDP-CFL: Cluster Federated Learning with Adaptive Clipping Threshold Differential Privacy

2024

Federated learning is a distributed machine learning approach that enables multiple clients to train models collaboratively. As its data remains stored locally on each client, this approach significantly enhances the protection of private information. However, …