Search Results for author: Sheng Chen

Found 37 papers, 4 papers with code

A Novel DeBERTa-based Model for Financial Question Answering Task

no code implementations12 Jul 2022 Yanbo J. Wang, Yuming Li, Hui Qin, Yuhang Guan, Sheng Chen

As a rising star in the field of natural language processing, question answering systems (Q&A Systems) are widely used in all walks of life.

Language Modelling Natural Language Processing +1

Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction

no code implementations20 May 2022 Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, Sheng Chen

However, the retrieval-based methods are sub-optimal and would cause more or less information losses, and it's difficult to balance the effectiveness and efficiency of the retrieval algorithm.

Click-Through Rate Prediction

AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System

no code implementations19 May 2022 Xiang Li, Xiaojiang Zhou, Yao Xiao, Peihao Huang, Dayao Chen, Sheng Chen, Yunsen Xian

Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages.

Information Retrieval Neural Architecture Search +2

Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication

no code implementations8 May 2022 Yang Wang, Zhen Gao, Dezhi Zheng, Sheng Chen, Deniz Gündüz, H. Vincent Poor

6G wireless networks are foreseen to speed up the convergence of the physical and cyber worlds and to enable a paradigm-shift in the way we deploy and exploit communication networks.

CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

no code implementations21 Dec 2021 Di Yao, Chang Gong, Lei Zhang, Sheng Chen, Jingping Bi

Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions.

CLIP2TV: Align, Match and Distill for Video-Text Retrieval

no code implementations10 Nov 2021 Zijian Gao, Jingyu Liu, Weiqi Sun, Sheng Chen, Dedan Chang, Lili Zhao

Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.

Ranked #2 on Video Retrieval on MSR-VTT (using extra training data)

Representation Learning Video-Text Retrieval

Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction

1 code implementation4 Nov 2021 Yuxin Meng, Eric Rigall, Xueen Chen, Feng Gao, Junyu Dong, Sheng Chen

Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns.

Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series

no code implementations18 Aug 2021 Jinhua Guo, Hao Chen, Jingxin Zhang, Sheng Chen

For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES).

Time Series

MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

1 code implementation13 May 2021 Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.

reinforcement-learning

Gaussian Dynamic Convolution for Efficient Single-Image Segmentation

no code implementations18 Apr 2021 Xin Sun, Changrui Chen, Xiaorui Wang, Junyu Dong, Huiyu Zhou, Sheng Chen

Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation.

Semantic Segmentation

Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

1 code implementation8 Jan 2021 Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang, Sheng Chen

To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise.

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search

no code implementations18 Dec 2020 Sheng Chen, Menghui Zhu, Deheng Ye, Weinan Zhang, Qiang Fu, Wei Yang

Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome.

Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

no code implementations25 Nov 2020 Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang

Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.

Towards Playing Full MOBA Games with Deep Reinforcement Learning

no code implementations NeurIPS 2020 Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.

Dota 2 League of Legends +1

Research on a New Convolutional Neural Network Model Combined with Random Edges Adding

no code implementations17 Mar 2020 Xuanyu Shu, Jin Zhang, Sen Tian, Sheng Chen, Lingyu Chen

Based on the idea of small world network, a random edge adding algorithm is proposed to improve the performance of convolutional neural network model.

DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators

no code implementations12 Jun 2019 Sheng Chen, Xu Wang, Chao Chen, Yifan Lu, Xijin Zhang, Linfu Wen

In this paper, we pursue very efficient neural network modules which can significantly boost the learning power of deep convolutional neural networks with negligible extra computational cost.

Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks

no code implementations4 Dec 2018 Sheng Chen, Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu

In this paper, we propose a learning-based low-overhead beam alignment method for vehicle-to-infrastructure communication in vehicular networks.

Neural Network simulation

Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach

no code implementations3 Dec 2018 Zhiyuan Jiang, Sheng Chen, Andreas F. Molisch, Rath Vannithamby, Sheng Zhou, Zhisheng Niu

Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.

Dimensionality Reduction

An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression

no code implementations NeurIPS 2018 Sheng Chen, Arindam Banerjee

To find the coefficient vector, estimators with a joint approximation of the noise covariance are often preferred than the simple linear regression in view of their superior empirical performance, which can be generally solved by alternating-minimization type procedures.

Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate

no code implementations1 Aug 2018 Sheng Chen, Jia Guo, Yang Liu, Xiang Gao, Zhen Han

In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer.

Face Recognition Robust Face Recognition

Sparse Linear Isotonic Models

no code implementations16 Oct 2017 Sheng Chen, Arindam Banerjee

In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors.

Additive models

Robust Structured Estimation with Single-Index Models

no code implementations ICML 2017 Sheng Chen, Arindam Banerjee

In this paper, we investigate general single-index models (SIMs) in high dimensions.

DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

no code implementations WS 2017 Sheng Chen, Akshay Soni, Aasish Pappu, Yashar Mehdad

Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work.

Multi-Label Learning TAG

Alternating Estimation for Structured High-Dimensional Multi-Response Models

no code implementations NeurIPS 2017 Sheng Chen, Arindam Banerjee

We consider learning high-dimensional multi-response linear models with structured parameters.

Structured Matrix Recovery via the Generalized Dantzig Selector

no code implementations NeurIPS 2016 Sheng Chen, Arindam Banerjee

In recent years, structured matrix recovery problems have gained considerable attention for its real world applications, such as recommender systems and computer vision.

Recommendation Systems

Structured Estimation with Atomic Norms: General Bounds and Applications

no code implementations NeurIPS 2015 Sheng Chen, Arindam Banerjee

For structured estimation problems with atomic norms, recent advances in the literature express sample complexity and estimation error bounds in terms of certain geometric measures, in particular Gaussian width of the unit norm ball, Gaussian width of a spherical cap induced by a tangent cone, and a restricted norm compatibility constant.

l1-norm Penalized Orthogonal Forward Regression

no code implementations4 Sep 2015 Xia Hong, Sheng Chen, Yi Guo, Junbin Gao

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).

Person Count Localization in Videos From Noisy Foreground and Detections

no code implementations CVPR 2015 Sheng Chen, Alan Fern, Sinisa Todorovic

This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections.

Human Detection Video Understanding

Estimation with Norm Regularization

no code implementations NeurIPS 2014 Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar

Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model.

Generalized Dantzig Selector: Application to the k-support norm

no code implementations NeurIPS 2014 Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee

For statistical analysis, we provide upper bounds for the Gaussian widths needed in the GDS analysis, yielding the first statistical recovery guarantee for estimation with the $k$-support norm.

Multi-Object Tracking via Constrained Sequential Labeling

no code implementations CVPR 2014 Sheng Chen, Alan Fern, Sinisa Todorovic

This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations.

Multi-Object Tracking

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