activity
20212024
most citedERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

37 citations · 73 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers

Ruiyuan Zhang, Jiaxiang Liu, Zexi Li +3

Geometric fracture assembly presents a challenging practical task in archaeology and 3D computer vision. Previous methods have focused solely on assembling fragments based on seman…

cs.DB20232 cited

The Fast and the Private: Task-based Dataset Search

Zezhou Huang, Jiaxiang Liu, Haonan Wang +1

Modern dataset search platforms employ ML task-based utility metrics instead of relying on metadata-based keywords to comb through extensive dataset repositories. In this setup, re…

cs.CV2023

Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution

Yiji Cheng, Fei Yin, Xiaoke Huang +5

Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance…

cs.CL20233 cited

ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization

Weixin Liu, Xuyi Chen, Jiaxiang Liu +4

Task-agnostic knowledge distillation attempts to address the problem of deploying large pretrained language model in resource-constrained scenarios by compressing a large pretraine…

cs.LG202225 cited

DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps

Jizhou Huang, Zhengjie Huang, Xiaomin Fang +5

Estimated time of arrival (ETA) prediction, also known as travel time estimation, is a fundamental task for a wide range of intelligent transportation applications, such as navigat…

cs.SE20226 cited

Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

Jiaxiang Liu, Yunhan Xing, Xiaomu Shi +3

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical dom…