Search Results for author: Jie Ma

Found 21 papers, 2 papers with code

Visualizing and Understanding Patch Interactions in Vision Transformer

no code implementations11 Mar 2022 Jie Ma, Yalong Bai, Bineng Zhong, Wei zhang, Ting Yao, Tao Mei

Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly through cross-patch information interactions.

On the Convergence of Clustered Federated Learning

no code implementations13 Feb 2022 Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang

Moreover, the paper proposes convergence analysis to the proposed clustered FL method.

Federated Learning

Pareto Policy Pool for Model-based Offline Reinforcement Learning

no code implementations ICLR 2022 Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi

Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model.

Offline RL reinforcement-learning

Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms

no code implementations CVPR 2021 Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang

Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.

DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis

no code implementations12 Jun 2021 Yujiao Wu, Jie Ma, Xiaoshui Huang, Sai Ho Ling, Steven Weidong Su

To improve the survival prediction accuracy and help prognostic decision-making in clinical practice for medical experts, we for the first time propose a multimodal deep learning method for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA.

Decision Making Multimodal Deep Learning +2

Extremal problems of Erdős, Faudree, Schelp and Simonovits on paths and cycles

no code implementations8 Feb 2021 Binlong Li, Jie Ma, Bo Ning

Many years ago, Erd\H{o}s, Faudree, Schelp and Simonovits proposed the study of the function $\phi(n, d, k)$, and conjectured that for any positive integers $n>d\geq k$, it holds that $\phi(n, d, k)\leq \lfloor\frac{k-1}{2}\rfloor\lfloor\frac{n}{d+1}\rfloor+\epsilon$, where $\epsilon=1$ if $k$ is odd and $\epsilon=2$ otherwise.


Structured Prediction as Translation between Augmented Natural Languages

1 code implementation ICLR 2021 Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos santos, Bing Xiang, Stefano Soatto

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.

Coreference Resolution Dialogue State Tracking +9

Improvements on induced subgraphs of given sizes

no code implementations11 Jan 2021 Jialin He, Jie Ma, Lilu Zhao

al. Our second result considers infinitely many pairs $(m, f)$ of special forms, whose exact values of $\sigma(m, f)$ were conjectured by Erd\H{o}s et.


When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network

no code implementations COLING 2020 Yipeng Yu, Ran Guan, Jie Ma, Zhuoxuan Jiang, Jingchang Huang

In online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface.

XTQA: Span-Level Explanations of the Textbook Question Answering

1 code implementation25 Nov 2020 Jie Ma, Jun Liu, Junjun Li, Qinghua Zheng, Qingyu Yin, Jianlong Zhou, Yi Huang

Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.

Question Answering

To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging

no code implementations EMNLP 2020 Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan

Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.

Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

no code implementations27 Jun 2020 Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Jie Ma, Jian Zhang

SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.

Data Augmentation

Some exact results on $4$-cycles: stability and supersaturation

no code implementations2 Dec 2019 Jialin He, Jie Ma, Tianchi Yang

A longstanding conjecture of Erd\H{o}s and Simonovits states that every $n$-vertex graph with $ex(n, C_4)+1$ edges contains at least $(1+o(1))\sqrt{n}$ 4-cycles.


Context-aware Attention Model for Coreference Resolution

no code implementations25 Sep 2019 Yufei Li, Xiangyu Zhou, Jie Ma, Yu Long, Xuan Wang, Chen Li

Coreference resolution is an important task for gaining more complete understanding about texts by artificial intelligence.

Coreference Resolution

Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability

no code implementations WS 2019 Zhuoxuan Jiang, Xian-Ling Mao, Ziming Huang, Jie Ma, Shaochun Li

Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.


Deep joint rain and haze removal from single images

no code implementations21 Jan 2018 Liang Shen, Zihan Yue, Quan Chen, Fan Feng, Jie Ma

On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil.

Rain Removal

MSR-net:Low-light Image Enhancement Using Deep Convolutional Network

no code implementations7 Nov 2017 Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, Jie Ma

In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.

Low-Light Image Enhancement

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