195 citations · 245 across the 11 of their papers we have counts for
12 papers
Cache Me If You Can: Accuracy-Aware Inference Engine for Differentially Private Data Exploration
Miti Mazmudar, Thomas Humphries, Jiaxiang Liu +2
Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent intera…
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Yuxiang Lu, Yiding Liu, Jiaxiang Liu +8
Neural retrievers based on pre-trained language models (PLMs), such as dual-encoders, have achieved promising performance on the task of open-domain question answering (QA). Their…
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention
Yang Liu, Jiaxiang Liu, Li Chen +7
Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information…
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Jin Hao, Jiaxiang Liu, Jin Li +17
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous…
Generalization Techniques Empirically Outperform Differential Privacy against Membership Inference
Jiaxiang Liu, Simon Oya, Florian Kerschbaum
Differentially private training algorithms provide protection against one of the most popular attacks in machine learning: the membership inference attack. However, these privacy a…
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
Yu Sun, Shuohuan Wang, Shikun Feng +19
Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-train…