1 code implementation • COLING 2022 • Minyu Chen, Guoqiang Li, Chen Ma, Jingyang Li, Hongfei Fu
Open-source platforms such as GitHub and Stack Overflow both play significant roles in current software ecosystems.
no code implementations • 6 May 2024 • Mira Slavcheva, Dave Gausebeck, Kevin Chen, David Buchhofer, Azwad Sabik, Chen Ma, Sachal Dhillon, Olaf Brandt, Alan Dolhasz
We propose a pipeline that leverages Stable Diffusion to improve inpainting results in the context of defurnishing -- the removal of furniture items from indoor panorama images.
1 code implementation • 21 Apr 2024 • Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, Ji-Rong Wen
Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.
1 code implementation • 12 Jan 2024 • Ziqiang Cui, Xing Tang, Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, Chen Ma
Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.
no code implementations • 22 Dec 2023 • Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu
A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.
no code implementations • 15 Dec 2023 • Chen Ma, Ningfei Wang, Qi Alfred Chen, Chao Shen
Our evaluation results show that the system-level effects can be significantly improved, i. e., the vehicle crash rate of SlowTrack is around 95% on average while existing works only have around 30%.
no code implementations • 24 Nov 2023 • Shengyin Sun, Yuxiang Ren, Chen Ma, Xuecang Zhang
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
1 code implementation • NeurIPS 2023 • Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, Xue Liu
In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.
1 code implementation • NeurIPS 2023 • Bowei He, Zexu Sun, Jinxin Liu, Shuai Zhang, Xu Chen, Chen Ma
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
no code implementations • 7 Oct 2023 • Zexu Sun, Bowei He, Ming Ma, Jiakai Tang, Yuchen Wang, Chen Ma, Dugang Liu
Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.
2 code implementations • 22 Aug 2023 • Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, ZhiYuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
no code implementations • 15 Aug 2023 • Bowei He, Xu He, Renrui Zhang, Yingxue Zhang, Ruiming Tang, Chen Ma
The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.
1 code implementation • 23 Jun 2023 • Chengmei Yang, Shuai Jiang, Bowei He, Chen Ma, Lianghua He
Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.
no code implementations • 8 May 2023 • Yankai Chen, Yifei Zhang, Menglin Yang, Zixing Song, Chen Ma, Irwin King
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models.
1 code implementation • 3 May 2023 • Xiong-Hui Chen, Bowei He, Yang Yu, Qingyang Li, Zhiwei Qin, Wenjie Shang, Jieping Ye, Chen Ma
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
no code implementations • 2 May 2023 • Yuening Wang, Yingxue Zhang, Antonios Valkanas, Ruiming Tang, Chen Ma, Jianye Hao, Mark Coates
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
1 code implementation • 21 Mar 2023 • Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.
no code implementations • 4 Feb 2023 • Fuyuan Lyu, Xing Tang, Dugang Liu, Haolun Wu, Chen Ma, Xiuqiang He, Xue Liu
Representation learning has been a critical topic in machine learning.
1 code implementation • 29 Dec 2022 • Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.
no code implementations • 24 Dec 2022 • Dan Liu, Xi Chen, Chen Ma, Xue Liu
Model quantization enables the deployment of deep neural networks under resource-constrained devices.
no code implementations • 7 Dec 2022 • Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu, Jian Lin, Tianpeng Wu, Ye Wang, Yu Fu, Lin Feng, Kangkang Gao, Zeyu Liu, Yuanzhe Pang, Chengqi Duan, Huipeng Zhou, Yajie Wang, Yuhang Zhao, Shangbo Wu, Haoran Lyu, Zhiyu Lin, YiFei Gao, Shuang Li, Haonan Wang, Jitao Sang, Chen Ma, Junhao Zheng, Yijia Li, Chao Shen, Chenhao Lin, Zhichao Cui, Guoshuai Liu, Huafeng Shi, Kun Hu, Mengxin Zhang
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
no code implementations • 11 Nov 2022 • Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
1 code implementation • 2 Aug 2022 • Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
no code implementations • 24 Jul 2022 • Can Chen, Xi Chen, Chen Ma, Zixuan Liu, Xue Liu
In this survey, we first give a formal definition of the gradient-based bi-level optimization.
no code implementations • 5 Jun 2022 • Yankai Chen, Huifeng Guo, Yingxue Zhang, Chen Ma, Ruiming Tang, Jingjie Li, Irwin King
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
no code implementations • 31 May 2022 • Can Chen, Chen Ma, Xi Chen, Sirui Song, Hao liu, Xue Liu
Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.
1 code implementation • 29 Apr 2022 • Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
1 code implementation • NeurIPS 2021 • Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, Yisen Wang
In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack.
no code implementations • 23 Jul 2021 • Dan Liu, Xi Chen, Jie Fu, Chen Ma, Xue Liu
To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.
no code implementations • 6 May 2021 • Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu
To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.
1 code implementation • 17 Apr 2021 • Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, Jackie Chi Kit Cheung
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
no code implementations • 13 Jan 2021 • Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
no code implementations • 15 Sep 2020 • Chen Ma, Shuyu Cheng, Li Chen, Jun Zhu, Junhai Yong
In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective function.
1 code implementation • CVPR 2021 • Chen Ma, Li Chen, Jun-Hai Yong
The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization.
no code implementations • 21 May 2020 • Hang Li, Chen Ma, Wei Xu, Xue Liu
Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications.
no code implementations • ICLR 2019 • Chen Ma, Dylan R. Ashley, Junfeng Wen, Yoshua Bengio
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
no code implementations • 1 Jan 2020 • Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
1 code implementation • 26 Dec 2019 • Chen Ma, Liheng Ma, Yingxue Zhang, Jianing Sun, Xue Liu, Mark Coates
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
no code implementations • 24 Dec 2019 • Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Müller
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.
Model-based Reinforcement Learning reinforcement-learning +1
2 code implementations • 7 Aug 2019 • Chen Ma
Latent Dirichlet Allocation (LDA) model is a famous model in the topic model field, it has been studied for years due to its extensive application value in industry and academia.
1 code implementation • 6 Aug 2019 • Chen Ma, Chenxu Zhao, Hailin Shi, Li Chen, Junhai Yong, Dan Zeng
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
2 code implementations • 21 Jun 2019 • Chen Ma, Peng Kang, Xue Liu
However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.
Ranked #1 on Recommendation Systems on Amazon-CDs (Recall@10 metric)
2 code implementations • 14 Dec 2018 • Chen Ma, Li Chen, Junhai Yong
(2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models.
Ranked #1 on Action Unit Detection on BP4D
1 code implementation • 7 Dec 2018 • Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, Xue Liu
In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.
1 code implementation • 27 Sep 2018 • Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.
no code implementations • 11 Apr 2018 • Chen Ma, Junfeng Wen, Yoshua Bengio
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks.
no code implementations • 27 Apr 2017 • Shichao Yang, Sandeep Konam, Chen Ma, Stephanie Rosenthal, Manuela Veloso, Sebastian Scherer
Second, predict the trajectory from the depth and normal.