74 citations · 110 across the 8 of their papers we have counts for
9 papers
: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties
Shihang Feng, Hanchen Wang, Chengyuan Deng +6
Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO sequestration and energy exploration (e.g.,…
Evaluating Stability in Massive Social Networks: Efficient Streaming Algorithms for Structural Balance
Vikrant Ashvinkumar, Sepehr Assadi, Chengyuan Deng +2
Structural balance theory studies stability in networks. Given a -vertex complete graph whose edges are labeled positive or negative, the graph is considered \emph{bal…
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu +21
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of app…
Differentially Private Range Query on Shortest Paths
Chengyuan Deng, Jie Gao, Jalaj Upadhyay +1
We consider differentially private range queries on a graph where query ranges are defined as the set of edges on a shortest path of the graph. Edges in the graph carry sensitive a…
On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion
Chengyuan Deng, Youzuo Lin
The data-driven approach has been demonstrated as a promising technique to solve complicated scientific problems. Full Waveform Inversion (FWI) is commonly epitomized as an image-t…
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
Chengyuan Deng, Shihang Feng, Hanchen Wang +6
Full waveform inversion (FWI) is widely used in geophysics to reconstruct high-resolution velocity maps from seismic data. The recent success of data-driven FWI methods results in…
On the Global Self-attention Mechanism for Graph Convolutional Networks
Chen Wang, Chengyuan Deng
Applying Global Self-attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutiona…
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations
Chen Wang, Chengyuan Deng, Vladimir Ivanov
Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations. The abili…
Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost
Chen Wang, Chengyuan Deng, Suzhen Wang
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification t…