Search Results for author: Peng Jiang

Found 82 papers, 28 papers with code

PA Ph&Tech at SemEval-2022 Task 11: NER Task with Ensemble Embedding from Reinforcement Learning

no code implementations SemEval (NAACL) 2022 Qizhi Lin, Changyu Hou, Xiaopeng Wang, Jun Wang, Yixuan Qiao, Peng Jiang, Xiandi Jiang, Benqi Wang, Qifeng Xiao

From pretrained contextual embedding to document-level embedding, the selection and construction of embedding have drawn more and more attention in the NER domain in recent research.

NER Zero-Shot Learning

RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

no code implementations6 Apr 2024 Yabin Zhang, Wenhui Yu, Erhan Zhang, Xu Chen, Lantao Hu, Peng Jiang, Kun Gai

For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.

Natural Language Understanding Sequential Recommendation

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

no code implementations4 Apr 2024 Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai

In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.

Contrastive Learning Multi-Task Learning +2

Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation

1 code implementation19 Mar 2024 Kasi Viswanath, Peng Jiang, Srikanth Saripalli

Additionally, we also investigate the possible benefits of using calibrated intensity in semantic segmentation in urban environments (SemanticKITTI) and cross-sensor domain adaptation.

Domain Adaptation LIDAR Semantic Segmentation +2

3DGS-ReLoc: 3D Gaussian Splatting for Map Representation and Visual ReLocalization

no code implementations17 Mar 2024 Peng Jiang, Gaurav Pandey, Srikanth Saripalli

This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting.

Visual Localization

Future Impact Decomposition in Request-level Recommendations

no code implementations29 Jan 2024 Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, Guangming Xie

Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.

Recommendation Systems

Off-Road LiDAR Intensity Based Semantic Segmentation

1 code implementation2 Jan 2024 Kasi Viswanath, Peng Jiang, Sujit PB, Srikanth Saripalli

LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning.

Autonomous Driving LIDAR Semantic Segmentation +2

GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction

no code implementations24 Nov 2023 Jia Huang, Peng Jiang, Alvika Gautam, Srikanth Saripalli

To our knowledge, this research is the first to conduct both quantitative and qualitative evaluations of VLMs in the context of pedestrian behavior prediction for autonomous driving.

Autonomous Driving Common Sense Reasoning +3

ROSS: Radar Off-road Semantic Segmentation

no code implementations20 Oct 2023 Peng Jiang, Srikanth Saripalli

As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential.

Autonomous Navigation Segmentation +1

AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

no code implementations6 Oct 2023 Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An

One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.

Reinforcement Learning (RL) Sequential Recommendation

KuaiSim: A Comprehensive Simulator for Recommender Systems

1 code implementation NeurIPS 2023 Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai

For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.

Reinforcement Learning (RL) Sequential Recommendation

Discrete Conditional Diffusion for Reranking in Recommendation

no code implementations14 Aug 2023 Xiao Lin, Xiaokai Chen, Chenyang Wang, Hantao Shu, Linfeng Song, Biao Li, Peng Jiang

To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation.

Recommendation Systems

A Large Language Model Enhanced Conversational Recommender System

no code implementations11 Aug 2023 Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun

For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.

Language Modelling Large Language Model +2

Measuring Item Global Residual Value for Fair Recommendation

1 code implementation17 Jul 2023 Jiayin Wang, Weizhi Ma, Chumeng Jiang, Min Zhang, Yuan Zhang, Biao Li, Peng Jiang

In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.

Recommendation Systems

Generative Flow Network for Listwise Recommendation

1 code implementation4 Jun 2023 Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian McAuley, Dong Zheng, Peng Jiang, Kun Gai

In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.

Recommendation Systems

Improving Extrinsics between RADAR and LIDAR using Learning

no code implementations17 May 2023 Peng Jiang, Srikanth Saripalli

This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems.

Autonomous Driving Sensor Fusion

An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint

no code implementations9 Feb 2023 Ziang Yan, Shusen Wang, Guorui Zhou, Jingjian Lin, Peng Jiang

Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.

Causal Inference Marketing

Exploration and Regularization of the Latent Action Space in Recommendation

1 code implementation7 Feb 2023 Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang

To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.

Recommendation Systems

Disentangled Causal Embedding With Contrastive Learning For Recommender System

1 code implementation7 Feb 2023 Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, Kun Gai

Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.

Contrastive Learning Recommendation Systems

Multi-Task Recommendations with Reinforcement Learning

1 code implementation7 Feb 2023 Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai

To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.

Multi-Task Learning Recommendation Systems +2

Two-Stage Constrained Actor-Critic for Short Video Recommendation

1 code implementation3 Feb 2023 Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai

One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.

Recommendation Systems reinforcement-learning +2

Reinforcing User Retention in a Billion Scale Short Video Recommender System

no code implementations3 Feb 2023 Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai

In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.

Recommendation Systems reinforcement-learning +1

PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement

1 code implementation6 Dec 2022 Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An

Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.

Recommendation Systems Reinforcement Learning (RL)

Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training

1 code implementation NIPS 2022 Peng Jiang, Lihan Hu, Shihui Song

At higher sparsity, our algorithm can still match the accuracy of nonstructured sparse training in most cases, while reducing the training time by up to 5x due to the fine-grained block structures in the models.

Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification

no code implementations28 Sep 2022 Peng Jiang, Juan Liu, Lang Wang, Zhihui Ynag, Hongyu Dong, Jing Feng

Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time.

feature selection Image Classification +1

Real-time Short Video Recommendation on Mobile Devices

no code implementations20 Aug 2022 Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, Kun Gai

However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate.

Recommendation Systems Re-Ranking

The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems

no code implementations4 Jul 2022 Haofeng Yuan, Peng Jiang, Shiji Song

In this paper, we propose an improved column generation algorithm with neural prediction (CG-P) for solving graph-based set covering problems.

Combinatorial Optimization Scheduling

Contrastive Learning of Features between Images and LiDAR

no code implementations24 Jun 2022 Peng Jiang, Srikanth Saripalli

Moreover, we conduct experiments on a real-world dataset to show the effectiveness of our loss function and network structure.

Contrastive Learning

Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation

no code implementations13 Jun 2022 Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang

We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved.

Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation

no code implementations10 Jun 2022 Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, Ji-Rong Wen

Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).

Disentanglement Re-Ranking

ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor

1 code implementation1 Jun 2022 Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang, Kun Gai, Bo An

Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.

Reinforcement Learning (RL) Sequential Recommendation

Constrained Reinforcement Learning for Short Video Recommendation

no code implementations26 May 2022 Qingpeng Cai, Ruohan Zhan, Chi Zhang, Jie Zheng, Guangwei Ding, Pinghua Gong, Dong Zheng, Peng Jiang

In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.

Recommendation Systems reinforcement-learning +1

A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

2 code implementations20 May 2022 Jillur Rahman Saurav, Mohammad Sadegh Nasr, Paul Koomey, Michael Robben, Manfred Huber, Jon Weidanz, Bríd Ryan, Eytan Ruppin, Peng Jiang, Jacob M. Luber

We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set.

Ethics Generative Adversarial Network +2

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

1 code implementation4 Apr 2022 Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, Peng Jiang

The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.

Causal Inference counterfactual +2

Deep Learning based Intelligent Coin-tap Test for Defect Recognition

1 code implementation20 Mar 2022 Hongyu Li, Peng Jiang, Tiejun Wang

This paper further develops transfer learning strategies for this issue, that is, to transfer the model trained on data of one scenario to another.

Domain Adaptation Pseudo Label +1

Local neural operator for solving transient partial differential equations on varied domains

1 code implementation11 Mar 2022 Hongyu Li, Ximeng Ye, Peng Jiang, Guoliang Qin, Tiejun Wang

For demonstration, LNO learns Navier-Stokes equations from randomly generated data samples, and then the pre-trained LNO is used as an explicit numerical time-marching scheme to solve the flow of fluid on unseen domains, e. g., the flow in a lid-driven cavity and the flow across the cascade of airfoils.

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

3 code implementations22 Feb 2022 Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, Tat-Seng Chua

The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.

Recommendation Systems User Simulation

Double-Barreled Question Detection at Momentive

no code implementations12 Feb 2022 Peng Jiang, Krishna Sumanth Muppalla, Qing Wei, Chidambara Natarajan Gopal, Chun Wang

We concluded that the word2vec subword embedding with maximum pooling is the optimal word embedding representation in terms of precision and running time in the offline experiments using the survey data at Momentive.

Active Learning BIG-bench Machine Learning

LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm

1 code implementation29 Jan 2022 Meng Ai, Biao Li, Heyang Gong, Qingwei Yu, Shengjie Xue, Yuan Zhang, Yunzhou Zhang, Peng Jiang

The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.

Distributed Computing Marketing

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

1 code implementation4 Jan 2022 Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, He Hu

To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion.

Contrastive Learning Recommendation Systems +2

SpSC: A Fast and Provable Algorithm for Sampling-Based GNN Training

no code implementations29 Sep 2021 Shihui Song, Peng Jiang

However, we find that SCO algorithms are impractical for training GNNs on large graphs because they need to store the moving averages of the aggregated features of all nodes in the graph.

SemCal: Semantic LiDAR-Camera Calibration using Neural MutualInformation Estimator

no code implementations21 Sep 2021 Peng Jiang, Philip Osteen, Srikanth Saripalli

This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information.

Camera Calibration Image Registration

PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic

no code implementations20 Aug 2021 Weicong Ding, Hanlin Tang, Jingshuo Feng, Lei Yuan, Sen yang, Guangxu Yang, Jie Zheng, Jing Wang, Qiang Su, Dong Zheng, Xuezhong Qiu, Yongqi Liu, Yuxuan Chen, Yang Liu, Chao Song, Dongying Kong, Kai Ren, Peng Jiang, Qiao Lian, Ji Liu

In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.

Recommendation Systems

Calibrating LiDAR and Camera using Semantic Mutual information

no code implementations24 Apr 2021 Peng Jiang, Philip Osteen, Srikanth Saripalli

We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information.

Image Registration

OFFSEG: A Semantic Segmentation Framework For Off-Road Driving

1 code implementation23 Mar 2021 Kasi Viswanath, Kartikeya Singh, Peng Jiang, Sujit P. B., Srikanth Saripalli

Off-road image semantic segmentation is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures.

Scene Understanding Segmentation +1

Extragalactic HI 21-cm absorption line observations with the Five-hundred-meter Aperture Spherical radio Telescope

no code implementations11 Mar 2021 Bo Zhang, Ming Zhu, Zhong-Zu Wu, Qing-Zheng Yu, Peng Jiang, You-Ling Yue, Meng-Lin Huang, Qiao-Li Hao

Our observations successfully confirmed the existence of HI absorption lines in all these systems, including two sources that were marginally detected by ALFALFA.

Astrophysics of Galaxies

Graph Attention Collaborative Similarity Embedding for Recommender System

no code implementations5 Feb 2021 Jinbo Song, Chao Chang, Fei Sun, Zhenyang Chen, Guoyong Hu, Peng Jiang

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning.

Collaborative Filtering Graph Attention +2

Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration

no code implementations19 Jan 2021 Peng Jiang, Rujia Wang, Bo Wu

Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications.

Graph Mining Databases Performance

Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks

no code implementations19 Jan 2021 Peng Jiang, Masuma Akter Rumi

However, we found that the existing neighbor sampling methods do not work well in a distributed setting.

Node Classification

RELLIS-3D Dataset: Data, Benchmarks and Analysis

3 code implementations17 Nov 2020 Peng Jiang, Philip Osteen, Maggie Wigness, Srikanth Saripalli

The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography.

3D Semantic Segmentation Autonomous Navigation +2

Scribble-Supervised Semantic Segmentation by Random Walk on Neural Representation and Self-Supervision on Neural Eigenspace

no code implementations11 Nov 2020 Zhiyi Pan, Peng Jiang, Changhe Tu

Moreover, given the probabilistic transition matrix, we apply the self-supervision on its eigenspace for consistency in the image's main parts.

Semantic Segmentation

NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation

no code implementations23 Oct 2020 Jinbo Song, Chao Chang, Fei Sun, Xinbo Song, Peng Jiang

To modeling the implicit correlations of neighbors in graph embedding aggregating, we propose a Neighbor-Aware Graph Attention Network for recommendation task, termed NGAT4Rec.

Collaborative Filtering Graph Attention +2

Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning

no code implementations13 Jul 2020 Peng Jiang, Gagan Agrawal

Compared with full-communication SGD, our ADPSGD achieves 1:14x to 1:27x speedups with a 100Gbps connection among computing nodes, and the speedups increase to 1:46x ~ 1:95x with a 10Gbps connection.

Image Classification Quantization

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

1 code implementation23 May 2020 Shijun Li, Wenqiang Lei, Qingyun Wu, Xiangnan He, Peng Jiang, Tat-Seng Chua

In this work, we consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively.

Collaborative Filtering Thompson Sampling

Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

no code implementations29 Mar 2020 Senlin Yang, Zhengfang Wang, Jing Wang, Anthony G. Cohn, Jia-Qi Zhang, Peng Jiang, Qingmei Sui

This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation.


LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

no code implementations2 Mar 2020 Peng Jiang, Srikanth Saripalli

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet).

Domain Adaptation Segmentation +1

GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining

no code implementations12 Dec 2019 Bin Liu, Yuxiao Ren, Hanchi Liu, Hui Xu, Zhengfang Wang, Anthony G. Cohn, Peng Jiang

The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries.

GPR Time Series Analysis

Compositional Network Embedding

no code implementations17 Apr 2019 Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang

Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.

Attribute Link Prediction +2

Personalized Re-ranking for Recommendation

1 code implementation15 Apr 2019 Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.

Recommendation Systems Re-Ranking

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

8 code implementations14 Apr 2019 Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang

To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.

Ranked #2 on Recommendation Systems on MovieLens 1M (HR@10 (full corpus) metric)

Sequential Recommendation

Deep Learning Inversion of Electrical Resistivity Data

no code implementations10 Apr 2019 Bin Liu, Qian Guo, Shucai Li, Benchao Liu, Yuxiao Ren, Yonghao Pang, Xu Guo, Lanbo Liu, Peng Jiang

According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.

Model Selection

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning +1

Deep-Learning Inversion of Seismic Data

no code implementations23 Jan 2019 Shucai Li, Bin Liu, Yuxiao Ren, Yangkang Chen, Senlin Yang, Yunhai Wang, Peng Jiang

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i. e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs).

Seismic Inversion Time Series Analysis

A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication

no code implementations NeurIPS 2018 Peng Jiang, Gagan Agrawal

The large communication overhead has imposed a bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) for training deep neural networks.


Super Diffusion for Salient Object Detection

no code implementations22 Nov 2018 Peng Jiang, Zhiyi Pan, Nuno Vasconcelos, Baoquan Chen, Jingliang Peng

Following this analysis, we propose super diffusion, a novel inclusive learning-based framework for salient object detection, which makes the optimum and robust performance by integrating a large pool of feature spaces, scales and even features originally computed for non-diffusion-based salient object detection.

Clustering Object +3

Multi-Source Pointer Network for Product Title Summarization

no code implementations21 Aug 2018 Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang

For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.

Sentence Sentence Summarization

DifNet: Semantic Segmentation by Diffusion Networks

no code implementations NeurIPS 2018 Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions.

Segmentation Semantic Segmentation

DiDA: Disentangled Synthesis for Domain Adaptation

no code implementations21 May 2018 Jinming Cao, Oren Katzir, Peng Jiang, Dani Lischinski, Danny Cohen-Or, Changhe Tu, Yangyan Li

The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.

Disentanglement Unsupervised Domain Adaptation

Generic Promotion of Diffusion-Based Salient Object Detection

no code implementations ICCV 2015 Peng Jiang, Nuno Vasconcelos, Jingliang Peng

In this work, we propose a generic scheme to promote any diffusion-based salient object detection algorithm by original ways to re-synthesize the diffusion matrix and construct the seed vector.

Clustering Object +3

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