Search Results for author: Zheng Cao

Found 19 papers, 4 papers with code

Optimizing Stock Option Forecasting with the Assembly of Machine Learning Models and Improved Trading Strategies

no code implementations29 Nov 2022 Zheng Cao, Raymond Guo, Wenyu Du, Jiayi Gao, Kirill V. Golubnichiy

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results.

Decision Making

CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

no code implementations21 Nov 2022 Yinpei Dai, Wanwei He, Bowen Li, Yuchuan Wu, Zheng Cao, Zhongqi An, Jian Sun, Yongbin Li

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data.

Goal-Oriented Dialog Retrieval

Lightweight Neural Network with Knowledge Distillation for CSI Feedback

no code implementations31 Oct 2022 Yiming Cui, Jiajia Guo, Zheng Cao, Huaze Tang, Chao-Kai Wen, Shi Jin, Xin Wang, Xiaolin Hou

Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder.

Knowledge Distillation

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

1 code implementation21 Oct 2022 ZeFeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.

Contrastive Learning SQL Parsing +1

Application of Deep Q Learning with Simulation Results for Elevator Optimization

no code implementations30 Sep 2022 Zheng Cao, Raymond Guo, Caesar M. Tuguinay, Mark Pock, Jiayi Gao, Ziyu Wang

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times.


Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting

no code implementations25 Aug 2022 Zheng Cao, Wenyu Du, Kirill V. Golubnichiy

Following results from the paper Quasi-Reversibility Method and Neural Network Machine Learning to Solution of Black-Scholes Equations (appeared on the AMS Contemporary Mathematics journal), we create and evaluate new empirical mathematical models for the Black-Scholes equation to analyze data for 92, 846 companies.

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

2 code implementations24 May 2022 Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.

Image Captioning Question Answering +5

Linking-Enhanced Pre-Training for Table Semantic Parsing

no code implementations18 Nov 2021 Bowen Qin, Lihan Wang, Binyuan Hui, Ruiying Geng, Zheng Cao, Min Yang, Jian Sun, Yongbin Li

Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation ability of the neural network.

Inductive Bias Language Modelling +2

AIBench Scenario: Scenario-distilling AI Benchmarking

no code implementations6 May 2020 Wanling Gao, Fei Tang, Jianfeng Zhan, Xu Wen, Lei Wang, Zheng Cao, Chuanxin Lan, Chunjie Luo, Xiaoli Liu, Zihan Jiang

We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.


Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO

no code implementations1 May 2020 Zheng Cao, Wan-Ting Shih, Jiajia Guo, Chao-Kai Wen, Shi Jin

We develop a DL based CSI feedback network in this study to complete the feedback of CSI effectively.

Information Theory Signal Processing Information Theory

Algorithmic Design and Implementation of Unobtrusive Multistatic Serial LiDAR Image

no code implementations8 Nov 2019 Chi Ding, Zheng Cao, Matthew S. Emigh, Jose C. Principe, Bing Ouyang, Anni Vuorenkoski, Fraser Dalgleish, Brian Ramos, Yanjun Li

To fully understand interactions between marine hydrokinetic (MHK) equipment and marine animals, a fast and effective monitoring system is required to capture relevant information whenever underwater animals appear.

Scene Understanding

AIBench: An Industry Standard Internet Service AI Benchmark Suite

no code implementations13 Aug 2019 Wanling Gao, Fei Tang, Lei Wang, Jianfeng Zhan, Chunxin Lan, Chunjie Luo, Yunyou Huang, Chen Zheng, Jiahui Dai, Zheng Cao, Daoyi Zheng, Haoning Tang, Kunlin Zhan, Biao Wang, Defei Kong, Tong Wu, Minghe Yu, Chongkang Tan, Huan Li, Xinhui Tian, Yatao Li, Junchao Shao, Zhenyu Wang, Xiaoyu Wang, Hainan Ye

On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.

Benchmarking Learning-To-Rank

BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite

no code implementations23 Feb 2018 Wanling Gao, Jianfeng Zhan, Lei Wang, Chunjie Luo, Daoyi Zheng, Xu Wen, Rui Ren, Chen Zheng, Xiwen He, Hainan Ye, Haoning Tang, Zheng Cao, Shujie Zhang, Jiahui Dai

On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.

Benchmarking Management

Marine Animal Classification with Correntropy Loss Based Multi-view Learning

no code implementations3 May 2017 Zheng Cao, Shujian Yu, Bing Ouyang, Fraser Dalgleish, Anni Vuorenkoski, Gabriel Alsenas, Jose Principe

Depending on the quantity and properties of acquired imagery, the animals are characterized as either features (shape, color, texture, etc.

General Classification MULTI-VIEW LEARNING

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