no code implementations • GWC 2016 • Amanda Hicks, Michael Rutherford, Christiane Fellbaum, Jiang Bian
While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender.
no code implementations • 26 Nov 2023 • Tianyu He, Junliang Guo, Runyi Yu, Yuchi Wang, Jialiang Zhu, Kaikai An, Leyi Li, Xu Tan, Chunyu Wang, Han Hu, HsiangTao Wu, Sheng Zhao, Jiang Bian
Zero-shot talking avatar generation aims at synthesizing natural talking videos from speech and a single portrait image.
1 code implementation • 3 Nov 2023 • Puheng Li, Zhong Li, Huishuai Zhang, Jiang Bian
This precisely elucidates the adverse effect of "modes shift" in ground truths on the model generalization.
1 code implementation • 23 Oct 2023 • Han Zhang, Xiaofan Gui, Shun Zheng, Ziheng Lu, Yuqi Li, Jiang Bian
Battery degradation remains a pivotal concern in the energy storage domain, with machine learning emerging as a potent tool to drive forward insights and solutions.
1 code implementation • 18 Oct 2023 • Dingyao Yu, Kaitao Song, Peiling Lu, Tianyu He, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian
For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks.
no code implementations • 17 Oct 2023 • Xu Yang, Xiao Yang, Weiqing Liu, Jinhui Li, Peng Yu, Zeqi Ye, Jiang Bian
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making.
no code implementations • 11 Oct 2023 • Amin Dada, Aokun Chen, Cheng Peng, Kaleb E Smith, Ahmad Idrissi-Yaghir, Constantin Marc Seibold, Jianning Li, Lars Heiliger, Xi Yang, Christoph M. Friedrich, Daniel Truhn, Jan Egger, Jiang Bian, Jens Kleesiek, Yonghui Wu
Traditionally, large language models have been either trained on general web crawls or domain-specific data.
no code implementations • 11 Oct 2023 • Han Zhang, Xumeng Wen, Shun Zheng, Wei Xu, Jiang Bian
Despite considerable efforts in developing effective learning models for tabular data, current transferable tabular models remain in their infancy, limited by either the lack of support for direct instruction following in new tasks or the neglect of acquiring foundational knowledge and capabilities from diverse tabular datasets.
no code implementations • 11 Oct 2023 • Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
no code implementations • 11 Oct 2023 • Jiawen Zhang, Xumeng Wen, Shun Zheng, Jia Li, Jiang Bian
Time-series forecasting serves as a linchpin in a myriad of applications, spanning various domains.
no code implementations • 10 Oct 2023 • Cheng Peng, Xi Yang, Kaleb E Smith, Zehao Yu, Aokun Chen, Jiang Bian, Yonghui Wu
We evaluated the transfer learning ability of the prompt-based learning algorithms in a cross-institution setting.
no code implementations • 8 Oct 2023 • Han Zhang, Yuqi Li, Shun Zheng, Ziheng Lu, Xiaofan Gui, Wei Xu, Jiang Bian
Here we introduce a universal deep learning approach that is capable of accommodating various aging conditions and facilitating effective learning under low-resource conditions by leveraging data from rich conditions.
1 code implementation • 6 Oct 2023 • Song Zhang, Qingzhong Wang, Jiang Bian, Haoyi Xiong
While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks.
no code implementations • 24 Sep 2023 • Haoyi Xiong, Jiang Bian, Sijia Yang, Xiaofei Zhang, Linghe Kong, Daqing Zhang
Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4.
no code implementations • 15 Sep 2023 • Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort.
no code implementations • 5 Sep 2023 • Yu Huang, Jingchuan Guo, William T Donahoo, Zhengkang Fan, Ying Lu, Wei-Han Chen, Huilin Tang, Lori Bilello, Elizabeth A Shenkman, Jiang Bian
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications.
no code implementations • 5 Sep 2023 • Yichong Leng, Zhifang Guo, Kai Shen, Xu Tan, Zeqian Ju, Yanqing Liu, Yufei Liu, Dongchao Yang, Leying Zhang, Kaitao Song, Lei He, Xiang-Yang Li, Sheng Zhao, Tao Qin, Jiang Bian
TTS approaches based on the text prompt face two main challenges: 1) the one-to-many problem, where not all details about voice variability can be described in the text prompt, and 2) the limited availability of text prompt datasets, where vendors and large cost of data labeling are required to write text prompts for speech.
no code implementations • 24 Aug 2023 • Yuxi Liu, Zhenhao Zhang, Shaowen Qin, Flora D. Salim, Antonio Jimeno Yepes, Jun Shen, Jiang Bian
In this paper, we propose a novel Hypergraph Convolutional Network that allows the representation of non-pairwise relationships among diagnosis codes in a hypergraph to capture the hidden feature structures so that fine-grained patient similarity can be calculated for personalized mortality risk prediction.
no code implementations • 16 Aug 2023 • Xianfeng Jiao, Zizhong Li, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian
To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios.
no code implementations • 8 Aug 2023 • Jan Egger, Christina Gsaxner, Xiaojun Chen, Jiang Bian, Jens Kleesiek, Behrus Puladi
At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro.
no code implementations • 6 Aug 2023 • Lei Song, Chuheng Zhang, Li Zhao, Jiang Bian
2)~How well can GPT-4 generalize to different scenarios for HVAC control?
no code implementations • 24 Jul 2023 • Gregor Stiglic, Leon Kopitar, Lucija Gosak, Primoz Kocbek, Zhe He, Prithwish Chakraborty, Pablo Meyer, Jiang Bian
The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts.
no code implementations • 6 Jul 2023 • Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu
Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets.
1 code implementation • 3 Jul 2023 • Chenfei Kang, Peiling Lu, Botao Yu, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian
In this paper, we propose EmoGen, an emotional music generation system that leverages a set of emotion-related music attributes as the bridge between emotion and music, and divides the generation into two stages: emotion-to-attribute mapping with supervised clustering, and attribute-to-music generation with self-supervised learning.
1 code implementation • 14 Jun 2023 • Jiawen Zhang, Shun Zheng, Wei Cao, Jiang Bian, Jia Li
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.
1 code implementation • 13 Jun 2023 • Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Jiang Bian
Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment.
1 code implementation • 6 Jun 2023 • Linjie Xu, Zhengyao Jiang, Jinyu Wang, Lei Song, Jiang Bian
Offline reinforcement learning (RL) methodologies enforce constraints on the policy to adhere closely to the behavior policy, thereby stabilizing value learning and mitigating the selection of out-of-distribution (OOD) actions during test time.
1 code implementation • 3 Jun 2023 • Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian
This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.
no code implementations • 31 May 2023 • Bei Li, Rui Wang, Junliang Guo, Kaitao Song, Xu Tan, Hany Hassan, Arul Menezes, Tong Xiao, Jiang Bian, Jingbo Zhu
Large language models (LLMs) have shown remarkable success across a wide range of natural language generation tasks, where proper prompt designs make great impacts.
1 code implementation • 31 May 2023 • Peiling Lu, Xin Xu, Chenfei Kang, Botao Yu, Chengyi Xing, Xu Tan, Jiang Bian
In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements.
1 code implementation • 22 May 2023 • Cheng Peng, Xi Yang, Aokun Chen, Kaleb E Smith, Nima PourNejatian, Anthony B Costa, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Gloria Lipori, Duane A Mitchell, Naykky S Ospina, Mustafa M Ahmed, William R Hogan, Elizabeth A Shenkman, Yi Guo, Jiang Bian, Yonghui Wu
This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.
1 code implementation • 18 May 2023 • Ang Lv, Xu Tan, Peiling Lu, Wei Ye, Shikun Zhang, Jiang Bian, Rui Yan
Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
1 code implementation • 28 Apr 2023 • Shufang Xie, Huishuai Zhang, Junliang Guo, Xu Tan, Jiang Bian, Hany Hassan Awadalla, Arul Menezes, Tao Qin, Rui Yan
In this paper, we propose ResiDual, a novel Transformer architecture with Pre-Post-LN (PPLN), which fuses the connections in Post-LN and Pre-LN together and inherits their advantages while avoids their limitations.
1 code implementation • 19 Apr 2023 • Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, Jiang Bian
We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP).
1 code implementation • 19 Apr 2023 • Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, Jiang Bian
Traveling Salesman Problem (TSP), as a classic routing optimization problem originally arising in the domain of transportation and logistics, has become a critical task in broader domains, such as manufacturing and biology.
no code implementations • 18 Apr 2023 • Kai Shen, Zeqian Ju, Xu Tan, Yanqing Liu, Yichong Leng, Lei He, Tao Qin, Sheng Zhao, Jiang Bian
To enhance the zero-shot capability that is important to achieve diverse speech synthesis, we design a speech prompting mechanism to facilitate in-context learning in the diffusion model and the duration/pitch predictor.
no code implementations • 3 Apr 2023 • Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian, Sheng Zhao
Recently, some diffusion-based methods achieved zero-shot audio editing by using a diffusion and denoising process conditioned on the text description of the output audio.
Ranked #3 on
Audio Generation
on AudioCaps
(FD metric, using extra
training data)
no code implementations • 31 Mar 2023 • Aokun Chen, Daniel Paredes, Zehao Yu, Xiwei Lou, Roberta Brunson, Jamie N. Thomas, Kimberly A. Martinez, Robert J. Lucero, Tanja Magoc, Laurence M. Solberg, Urszula A. Snigurska, Sarah E. Ser, Mattia Prosperi, Jiang Bian, Ragnhildur I. Bjarnadottir, Yonghui Wu
To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes.
no code implementations • 30 Mar 2023 • Chenpeng Du, Qi Chen, Tianyu He, Xu Tan, Xie Chen, Kai Yu, Sheng Zhao, Jiang Bian
Additionally, we propose a novel method for generating continuous video frames with the DDIM image decoder trained on individual frames, eliminating the need for modelling the joint distribution of consecutive frames directly.
no code implementations • ICCV 2023 • Zenghao Chai, Tianke Zhang, Tianyu He, Xu Tan, Tadas Baltrušaitis, HsiangTao Wu, Runnan Li, Sheng Zhao, Chun Yuan, Jiang Bian
3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image.
Ranked #1 on
3D Face Reconstruction
on REALY (side-view)
no code implementations • 14 Mar 2023 • Cheng Peng, Xi Yang, Zehao Yu, Jiang Bian, William R. Hogan, Yonghui Wu
GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the two datasets by 1%~3% and 0. 7%~1. 3%, respectively.
Clinical Concept Extraction
Machine Reading Comprehension
+2
no code implementations • 14 Mar 2023 • Aokun Chen, Zehao Yu, Xi Yang, Yi Guo, Jiang Bian, Yonghui Wu
Materials and methods: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes.
1 code implementation • 7 Mar 2023 • Shantanu Ghosh, Zheng Feng, Jiang Bian, Kevin Butler, Mattia Prosperi
DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions.
no code implementations • 24 Feb 2023 • Yuxuan Zhang, Qingzhong Wang, Jiang Bian, Yi Liu, Yanwu Xu, Dejing Dou, Haoyi Xiong
Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification?
no code implementations • 13 Feb 2023 • Kai Shen, Junliang Guo, Xu Tan, Siliang Tang, Rui Wang, Jiang Bian
This paper sheds light on the following points: 1) Softmax and ReLU use different normalization methods over elements which lead to different variances of results, and ReLU is good at dealing with a large number of key-value slots; 2) FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key-value memories.
no code implementations • 21 Jan 2023 • Xu Tan, Tao Qin, Jiang Bian, Tie-Yan Liu, Yoshua Bengio
Regeneration learning extends the concept of representation learning to data generation tasks, and can be regarded as a counterpart of traditional representation learning, since 1) regeneration learning handles the abstraction (Y') of the target data Y for data generation while traditional representation learning handles the abstraction (X') of source data X for data understanding; 2) both the processes of Y'-->Y in regeneration learning and X-->X' in representation learning can be learned in a self-supervised way (e. g., pre-training); 3) both the mappings from X to Y' in regeneration learning and from X' to Y in representation learning are simpler than the direct mapping from X to Y.
1 code implementation • 30 Dec 2022 • Zehua Chen, Yihan Wu, Yichong Leng, Jiawei Chen, Haohe Liu, Xu Tan, Yang Cui, Ke Wang, Lei He, Sheng Zhao, Jiang Bian, Danilo Mandic
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples.
1 code implementation • 19 Dec 2022 • Qingrui Jia, Xuhong LI, Lei Yu, Jiang Bian, Penghao Zhao, Shupeng Li, Haoyi Xiong, Dejing Dou
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power.
1 code implementation • 19 Dec 2022 • Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, Linli Xu
Diffusion models have achieved state-of-the-art synthesis quality on both visual and audio tasks, and recent works further adapt them to textual data by diffusing on the embedding space.
no code implementations • 15 Dec 2022 • Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand.
no code implementations • 9 Dec 2022 • Anni Tang, Tianyu He, Xu Tan, Jun Ling, Runnan Li, Sheng Zhao, Li Song, Jiang Bian
More specifically, the implicit memory is employed in the audio-to-expression model to capture high-level semantics in the audio-expression shared space, while the explicit memory is employed in the neural-rendering model to help synthesize pixel-level details.
no code implementations • 6 Dec 2022 • Zehao Yu, Xi Yang, Chong Dang, Prakash Adekkanattu, Braja Gopal Patra, Yifan Peng, Jyotishman Pathak, Debbie L. Wilson, Ching-Yuan Chang, Wei-Hsuan Lo-Ciganic, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i. e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i. e., opioid use), and evaluate the extraction rate of SDoH using cancer populations.
1 code implementation • 5 Dec 2022 • Yuanying Cai, Chuheng Zhang, Li Zhao, Wei Shen, Xuyun Zhang, Lei Song, Jiang Bian, Tao Qin, TieYan Liu
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
no code implementations • 3 Dec 2022 • Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
To reduce the compute time overhead of private learning, we show that \emph{per-layer clipping}, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization.
1 code implementation • 30 Nov 2022 • Yihan Wu, Junliang Guo, Xu Tan, Chen Zhang, Bohan Li, Ruihua Song, Lei He, Sheng Zhao, Arul Menezes, Jiang Bian
In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech.
no code implementations • 9 Oct 2022 • Yukun Zheng, Jiang Bian, Guanghao Meng, Chao Zhang, Honggang Wang, Zhixuan Zhang, Sen Li, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng
These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval.
1 code implementation • 19 Sep 2022 • Nicholas Gray, Megan Moraes, Jiang Bian, Allen Tian, Alex Wang, Haoyi Xiong, Zhishan Guo
Real-time machine learning detection algorithms are often found within autonomous vehicle technology and depend on quality datasets.
no code implementations • 3 Sep 2022 • Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
1 code implementation • 21 Aug 2022 • Ashkan Farhangi, Jiang Bian, Arthur Huang, Haoyi Xiong, Jun Wang, Zhishan Guo
Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.
no code implementations • 26 Jul 2022 • Jiang Bian, Qingzhong Wang, Haoyi Xiong, Jun Huang, Chen Liu, Xuhong LI, Jun Cheng, Jun Zhao, Feixiang Lu, Dejing Dou
While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.
no code implementations • 23 Jul 2022 • Zheng Feng, Mattia Prosperi, Jiang Bian
Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias.
no code implementations • 15 Jul 2022 • Inyoung Jun, Simone Marini, Christina A. Boucher, J. Glenn Morris, Jiang Bian, Mattia Prosperi
ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects.
no code implementations • 4 Jul 2022 • Tianping Zhang, Yizhuo Zhang, Wei Cao, Jiang Bian, Xiaohan Yi, Shun Zheng, Jian Li
It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset.
no code implementations • 19 Jun 2022 • Wenlei Shi, Xinquan Huang, Xiaotian Gao, Xinran Wei, Jia Zhang, Jiang Bian, Mao Yang, Tie-Yan Liu
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE).
no code implementations • 20 May 2022 • Qingzhong Wang, Haifang Li, Haoyi Xiong, Wen Wang, Jiang Bian, Yu Lu, Shuaiqiang Wang, Zhicong Cheng, Dejing Dou, Dawei Yin
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
1 code implementation • 20 May 2022 • Liang Shen, Zhihua Wu, Weibao Gong, Hongxiang Hao, Yangfan Bai, HuaChao Wu, Xinxuan Wu, Jiang Bian, Haoyi Xiong, dianhai yu, Yanjun Ma
With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.
no code implementations • 19 May 2022 • Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li
Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.
1 code implementation • ICLR 2022 • Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
no code implementations • 22 Feb 2022 • Zhe He, Shubo Tian, Arslan Erdengasileng, Neil Charness, Jiang Bian
Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management.
1 code implementation • 18 Feb 2022 • Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, LiWei Wang, Tie-Yan Liu
In this work, we develop a novel approach that can significantly accelerate the training of Physics-Informed Neural Networks.
no code implementations • 18 Feb 2022 • Lin Huang, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu
How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.
no code implementations • 18 Feb 2022 • Lin Huang, Qiyuan Dong, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu
As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.
no code implementations • 2 Feb 2022 • Xi Yang, Aokun Chen, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Christopher A Harle, Gloria Lipori, Duane A Mitchell, William R Hogan, Elizabeth A Shenkman, Jiang Bian, Yonghui Wu
GatorTron models scale up the clinical language model from 110 million to 8. 9 billion parameters and improve 5 clinical NLP tasks (e. g., 9. 6% and 9. 5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.
1 code implementation • 11 Jan 2022 • Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian
To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.
1 code implementation • 12 Dec 2021 • Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu
Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.
2 code implementations • COLING 2022 • Zhiping Luo, Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering.
1 code implementation • 24 Nov 2021 • Zhining Liu, Pengfei Wei, Zhepei Wei, Boyang Yu, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang
Class-imbalance is a common problem in machine learning practice.
2 code implementations • 26 Oct 2021 • Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu
To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.
7 code implementations • IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2021 • Ruipeng Zhang, Mengjun Xie, Jiang Bian
The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years.
no code implementations • 29 Sep 2021 • Mingxiao Feng, Guozi Liu, Li Zhao, Lei Song, Jiang Bian, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu
We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.
no code implementations • 29 Sep 2021 • Zhengyu Yang, Kan Ren, Xufang Luo, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li
Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).
1 code implementation • 14 Sep 2021 • Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu
In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.
no code implementations • 11 Aug 2021 • Weishen Pan, Sen Cui, Jiang Bian, ChangShui Zhang, Fei Wang
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently.
no code implementations • 10 Aug 2021 • Zehao Yu, Xi Yang, Chong Dang, Songzi Wu, Prakash Adekkanattu, Jyotishman Pathak, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu
In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes).
1 code implementation • 19 Jul 2021 • Xi Yang, Zehao Yu, Yi Guo, Jiang Bian, Yonghui Wu
The goal of this study is to systematically explore three widely used transformer-based models (i. e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain.
no code implementations • 12 Jul 2021 • Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian
Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.
no code implementations • 6 Jul 2021 • Mattia Prosperi, Simone Marini, Christina Boucher, Jiang Bian
Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility.
2 code implementations • 24 Jun 2021 • Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian
In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.
no code implementations • 21 Jun 2021 • Yefeng Wang, Yunpeng Zhao, Jiang Bian, Rui Zhang
We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i. e., iDISK).
1 code implementation • 17 Jun 2021 • Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
no code implementations • 9 Apr 2021 • Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu
We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.
no code implementations • 4 Apr 2021 • Esha Singh, Anu Bompelli, Ruyuan Wan, Jiang Bian, Serguei Pakhomov, Rui Zhang
Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively.
1 code implementation • 19 Mar 2021 • Xuhong LI, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, Dejing Dou
Then, to understand the interpretation results, we also survey the performance metrics for evaluating interpretation algorithms.
no code implementations • 8 Mar 2021 • Xia Hu, Lingyang Chu, Jian Pei, Weiqing Liu, Jiang Bian
Model complexity is a fundamental problem in deep learning.
no code implementations • 15 Feb 2021 • Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu
To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.
no code implementations • 28 Jan 2021 • Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu
As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.
no code implementations • 22 Jan 2021 • Zhaoyi Chen, Xiong Liu, William Hogan, Elizabeth Shenkman, Jiang Bian
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development.
no code implementations • 1 Jan 2021 • Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian
In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.
1 code implementation • 1 Jan 2021 • Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
1 code implementation • 24 Dec 2020 • Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian, Tie-Yan Liu
While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.
1 code implementation • 11 Dec 2020 • Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.
no code implementations • 19 Oct 2020 • Hao Wang, Jia Zhang, Yingce Xia, Jiang Bian, Chao Zhang, Tie-Yan Liu
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
2 code implementations • NeurIPS 2020 • Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang
This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
2 code implementations • 22 Sep 2020 • Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
no code implementations • 9 Jul 2020 • Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.
no code implementations • 16 Jun 2020 • Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei
Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.
1 code implementation • 10 Jun 2020 • Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Li-Wei Wang, Jiang Bian, Tie-Yan Liu
Pre-trained contextual representations (e. g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks.
9 code implementations • ECCV 2020 • Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.
no code implementations • 26 Mar 2020 • Yunpeng Zhao, Mattia Prosperi, Tianchen Lyu, Yi Guo, Jiang Bian
Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.
no code implementations • 13 Nov 2019 • Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.
6 code implementations • NeurIPS 2019 • Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
no code implementations • 1 Oct 2019 • Xi Yang, Yan Gong, Nida Waheed, Keith March, Jiang Bian, William R. Hogan, Yonghui Wu
Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life.
no code implementations • 25 Sep 2019 • Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minglie Huang, Tao Qin, Tie-Yan Liu
Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.
no code implementations • 25 Sep 2019 • Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu
One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.
1 code implementation • 8 Sep 2019 • Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu
To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.
no code implementations • 25 Aug 2019 • Ziyu Liu, Guolin Ke, Jiang Bian, Tie-Yan Liu
Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way.
2 code implementations • 16 Jul 2019 • Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu
Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.
no code implementations • 6 Jul 2019 • Hansi Zhang, Christopher Wheldon, Adam G. Dunn, Cui Tao, Jinhai Huo, Rui Zhang, Mattia Prosperi, Yi Guo, Jiang Bian
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
no code implementations • 22 May 2019 • Francois Modave, Yunpeng Zhao, Janice Krieger, Zhe He, Yi Guo, Jinhai Huo, Mattia Prosperi, Jiang Bian
Among American women, the rate of breast cancer is only second to lung cancer.
no code implementations • ICLR 2019 • Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, Tie-Yan Liu
Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.
no code implementations • 28 Apr 2019 • Seyedeh Neelufar Payrovnaziri, Laura A. Barrett, Daniel Bis, Jiang Bian, Zhe He
Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care.
2 code implementations • IJCNLP 2019 • Shun Zheng, Wei Cao, Wei Xu, Jiang Bian
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope.
Ranked #4 on
Document-level Event Extraction
on ChFinAnn
1 code implementation • 2 Mar 2019 • Xihan Li, Jia Zhang, Jiang Bian, Yunhai Tong, Tie-Yan Liu
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
no code implementations • 12 Dec 2018 • Laura A. Barrett, Seyedeh Neelufar Payrovnaziri, Jiang Bian, Zhe He
Heart disease remains the leading cause of death in the United States.
no code implementations • 20 Feb 2018 • Jiang Bian, Dayong Tian, Yuanyan Tang, DaCheng Tao
This paper comprehensively surveys the development of trajectory clustering.
4 code implementations • 6 Dec 2017 • Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, Tie-Yan Liu
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
no code implementations • 15 Nov 2017 • Jiang Bian, Haoyi Xiong, Yanjie Fu, Sajal K. Das
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}.
no code implementations • 27 Sep 2017 • Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu
However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.
1 code implementation • ICML 2017 • Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu
Many supervised learning tasks are emerged in dual forms, e. g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation.
no code implementations • 25 Apr 2017 • Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, Zhishan Guo
Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e. g., covariance matrix), and the "linear inseparability" of EHR data.
no code implementations • 28 Feb 2017 • Yang Fan, Fei Tian, Tao Qin, Jiang Bian, Tie-Yan Liu
Machine learning is essentially the sciences of playing with data.
no code implementations • 18 Oct 2016 • Sijia Yang, Haoyi Xiong, Kaibo Xu, Licheng Wang, Jiang Bian, Zeyi Sun
In this paper, we revised the problem of predictive analysis of disease using personal EHR data and LDA classifier.
no code implementations • 2 Jun 2016 • Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu
In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.
no code implementations • 29 May 2015 • Huazheng Wang, Fei Tian, Bin Gao, Jiang Bian, Tie-Yan Liu
Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries.
no code implementations • 7 Jul 2014 • Bin Gao, Jiang Bian, Tie-Yan Liu
In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding.
no code implementations • 7 Jul 2014 • Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Tie-Yan Liu
In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.
no code implementations • 23 Apr 2014 • Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu
Click prediction is one of the fundamental problems in sponsored search.