Search Results for author: Peng Jin

Found 15 papers, 3 papers with code

OpenFWI: Benchmark Seismic Datasets for Machine Learning-Based Full Waveform Inversion

no code implementations4 Nov 2021 Chengyuan Deng, Yinan Feng, Shihang Feng, Peng Jin, Xitong Zhang, Qili Zeng, Youzuo Lin

OpenFWI is the first-of-its-kind in the geoscience and machine learning community to facilitate diversified, rigorous, and reproducible research on machine learning-based FWI.

Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop

no code implementations14 Oct 2021 Peng Jin, Xitong Zhang, Yinpeng Chen, Sharon Xiaolei Huang, Zicheng Liu, Youzuo Lin

In particular, we use finite difference to approximate the forward modeling of PDE as a differentiable operator (from velocity map to seismic data) and model its inversion by CNN (from seismic data to velocity map).

Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning

no code implementations13 Jun 2021 Peng Jin, Min Zhang, Jianwen Li, Li Han, Xuejun Wen

Formally verifying Deep Reinforcement Learning (DRL) systems is a challenging task due to the dynamic continuity of system behaviors and the black-box feature of embedded neural networks.

Robust Kalman filter-based dynamic state estimation of natural gas pipeline networks

no code implementations26 Feb 2021 Liang Chen, Peng Jin, Jing Yang, Yang Li, Yi Song

To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper.

CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples

no code implementations SEMEVAL 2020 Yice Zhang, Jiaxuan Lin, Yang Fan, Peng Jin, Yuanchao Liu, Bingquan Liu

For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements.

Knowledge Graphs

Artificial Intelligence Enabled Traffic Monitoring System

no code implementations2 Oct 2020 Vishal Mandal, Abdul Rashid Mussah, Peng Jin, Yaw Adu-Gyamfi

Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts.

Real-Time Object Detection

Distributional Discrepancy: A Metric for Unconditional Text Generation

1 code implementation4 May 2020 Ping Cai, Xingyuan Chen, Peng Jin, Hongjun Wang, Tianrui Li

The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data.

Language Modelling Text Generation

Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation

1 code implementation5 Apr 2020 Xingyuan Chen, Ping Cai, Peng Jin, Hongjun Wang, Xin-yu Dai, Jia-Jun Chen

To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely.

Language Modelling Text Generation

Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses

no code implementations20 Oct 2019 Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi, William G. Buttlar

Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community.

The Detection of Distributional Discrepancy for Text Generation

no code implementations28 Sep 2019 Xingyuan Chen, Ping Cai, Peng Jin, Haokun Du, Hongjun Wang, Xingyu Dai, Jia-Jun Chen

In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text.

Language Modelling Text Generation

Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints

no code implementations13 Jan 2017 Jielei Chu, Hongjun Wang, Hua Meng, Peng Jin, Tianrui Li

To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints restricted Boltzmann machine with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by pairwise constraints and the process of encoding is conducted under these guidances.

CLTC: A Chinese-English Cross-lingual Topic Corpus

no code implementations LREC 2012 Yunqing Xia, Guoyu Tang, Peng Jin, Xia Yang

A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods.

Text Clustering

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