Search Results for author: Jiang Bian

Found 141 papers, 51 papers with code

An Analysis of WordNet’s Coverage of Gender Identity Using Twitter and The National Transgender Discrimination Survey

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.

GAIA: Zero-shot Talking Avatar Generation

no code implementations26 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.

On the Generalization Properties of Diffusion Models

1 code implementation3 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.

BatteryML:An Open-source platform for Machine Learning on Battery Degradation

1 code implementation23 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.

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models

1 code implementation18 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.

Music Classification

Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle

no code implementations17 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.

Anomaly Detection Decision Making +2

Towards Foundation Models for Learning on Tabular Data

no code implementations11 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.

Instruction Following Language Modelling +1

NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining

no code implementations11 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.

Learning Semantic Representations Temporal Sequences +1

ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting

no code implementations11 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.

Time Series Time Series Forecasting

Accurate battery lifetime prediction across diverse aging conditions with deep learning

no code implementations8 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.

Single-cell modeling

TiC: Exploring Vision Transformer in Convolution

1 code implementation6 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.

Image Classification

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

no code implementations24 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.

Natural Language Understanding Scheduling

Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

no code implementations15 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.

Evolutionary Algorithms

Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)

no code implementations5 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.


PromptTTS 2: Describing and Generating Voices with Text Prompt

no code implementations5 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.

Language Modelling Large Language Model

Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction

no code implementations24 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.

Decision Making

Microstructure-Empowered Stock Factor Extraction and Utilization

no code implementations16 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.

Stock Trend Prediction

Pre-Trained Large Language Models for Industrial Control

no code implementations6 Aug 2023 Lei Song, Chuheng Zhang, Li Zhao, Jiang Bian

2)~How well can GPT-4 generalize to different scenarios for HVAC control?

Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI

no code implementations24 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.

Extreme Summarization

Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance

no code implementations6 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.

Reinforcement Learning (RL)

EmoGen: Eliminating Subjective Bias in Emotional Music Generation

1 code implementation3 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.

Clustering Music Generation +1

Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series

1 code implementation14 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.

Irregular Time Series Representation Learning +1

A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

1 code implementation13 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.

Autonomous Driving Management +2

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

1 code implementation6 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.

Offline RL reinforcement-learning +2

UADB: Unsupervised Anomaly Detection Booster

1 code implementation3 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.

Unsupervised Anomaly Detection

Deliberate then Generate: Enhanced Prompting Framework for Text Generation

no code implementations31 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.

Text Generation

MuseCoco: Generating Symbolic Music from Text

1 code implementation31 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.

Audio Generation Music Generation

GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework

1 code implementation18 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.

Denoising Music Generation

ResiDual: Transformer with Dual Residual Connections

1 code implementation28 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.

Machine Translation

H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

1 code implementation19 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).

Hierarchical Reinforcement Learning reinforcement-learning

Pointerformer: Deep Reinforced Multi-Pointer Transformer for the Traveling Salesman Problem

1 code implementation19 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.

Traveling Salesman Problem

NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers

no code implementations18 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.

Speech Synthesis

AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models

no code implementations3 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)

Audio Generation Denoising +1

Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing

no code implementations31 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.

Language Modelling Large Language Model

DAE-Talker: High Fidelity Speech-Driven Talking Face Generation with Diffusion Autoencoder

no code implementations30 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.

Talking Face Generation

Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension

no code implementations14 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

Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures

no code implementations14 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.

Classification Language Modelling +1

DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data

1 code implementation7 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.

Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks

no code implementations24 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?

Classification Data Augmentation +3

A Study on ReLU and Softmax in Transformer

no code implementations13 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.

Document Translation

Regeneration Learning: A Learning Paradigm for Data Generation

no code implementations21 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.

Image Generation Representation Learning +6

ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech

1 code implementation30 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.


Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features

1 code implementation19 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.

Difformer: Empowering Diffusion Models on the Embedding Space for Text Generation

1 code implementation19 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.

Denoising Machine Translation +2

Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management

no code implementations15 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.

Management Multi-agent Reinforcement Learning +2

Memories are One-to-Many Mapping Alleviators in Talking Face Generation

no code implementations9 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.

Neural Rendering Talking Face Generation

SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies

no code implementations6 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.

TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets

1 code implementation5 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.

D4RL Offline RL +2

Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

no code implementations3 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.

VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing

1 code implementation30 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.

Machine Translation speech-recognition +3

Multi-Objective Personalized Product Retrieval in Taobao Search

no code implementations9 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.

Collaborative Filtering Retrieval

GLARE: A Dataset for Traffic Sign Detection in Sun Glare

1 code implementation19 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.

Traffic Sign Detection Traffic Sign Recognition

Learning Differential Operators for Interpretable Time Series Modeling

no code implementations3 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.

Decision Making Meta-Learning +2

AA-Forecast: Anomaly-Aware Forecast for Extreme Events

1 code implementation21 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.

Management Time Series +1

$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

no code implementations26 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.

Action Detection Action Localization +2

Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data

no code implementations23 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.

LordNet: Learning to Solve Parametric Partial Differential Equations without Simulated Data

no code implementations19 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).

A Simple yet Effective Framework for Active Learning to Rank

no code implementations20 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.

Active Learning Learning-To-Rank

SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System

1 code implementation20 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.

Distributed Computing

Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

no code implementations19 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.

reinforcement-learning Reinforcement Learning (RL)

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

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.

Scheduling Time Series +1

Learning Physics-Informed Neural Networks without Stacked Back-propagation

1 code implementation18 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.

Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

no code implementations18 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.

Graph Learning Time Series +1

AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation

no code implementations18 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.

Segmentation Semantic Segmentation

GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

no code implementations2 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.

Clinical Concept Extraction Language Modelling +5

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

1 code implementation11 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.

Stock Prediction

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

1 code implementation12 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.

Graph Embedding Node Classification

KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings

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.

Contrastive Learning Knowledge Graph Embedding +6

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

2 code implementations26 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.

Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management

no code implementations29 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.

Management Multi-agent Reinforcement Learning +2

Deep Ensemble Policy Learning

no code implementations29 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).

Ensemble Learning Reinforcement Learning (RL)

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

1 code implementation14 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.

Multivariate Time Series Forecasting Time Series

Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition

no code implementations11 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.

Fairness Feature Importance

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models

no code implementations10 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).

Clinical Relation Extraction Using Transformer-based Models

1 code implementation19 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.

Binary Classification Classification +2

Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation

no code implementations12 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.

Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

no code implementations6 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.

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

2 code implementations24 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.

Stock Prediction

Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter

no code implementations21 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).

Relation Extraction Word Embeddings

Deep Subdomain Adaptation Network for Image Classification

1 code implementation17 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.

Classification Domain Adaptation +4

Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study

no code implementations9 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.

A Conversational Agent System for Dietary Supplements Use

no code implementations4 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.

Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond

1 code implementation19 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.

Adversarial Robustness

REST: Relational Event-driven Stock Trend Forecasting

no code implementations15 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.

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 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.

Algorithmic Trading reinforcement-learning +1

Applications of artificial intelligence in drug development using real-world data

no code implementations22 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.

Event Detection

LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

no code implementations1 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.

Graph Representation Learning

Dynamic Graph Representation Learning with Fourier Temporal State Embedding

1 code implementation1 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.

Graph Embedding Graph Representation Learning

Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations

1 code implementation24 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.

Graph Attention Multi-agent Reinforcement Learning

ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

1 code implementation11 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.


COSEA: Convolutional Code Search with Layer-wise Attention

no code implementations19 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.

Code Search

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

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.

imbalanced classification Meta-Learning

Qlib: An AI-oriented Quantitative Investment Platform

2 code implementations22 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.

Portfolio Optimization Stock Market Prediction

Learning to Reweight with Deep Interactions

no code implementations9 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.

Image Classification Machine Translation +1

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 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.

MC-BERT: Efficient Language Pre-Training via a Meta Controller

1 code implementation10 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.

Binary Classification Cloze Test +4

Invertible Image Rescaling

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.

Image Super-Resolution

Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets

no code implementations26 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.

Active Learning General Classification

Federated Learning for Healthcare Informatics

no code implementations13 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.

Federated Learning

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

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)

Atari Games Distributional Reinforcement Learning +2

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods

no code implementations1 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.

BIG-bench Machine Learning Specificity

Independence-aware Advantage Estimation

no code implementations25 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.

Demonstration Actor Critic

no code implementations25 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.

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 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.

Classification General Classification +1

LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm

no code implementations25 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.

Classification General Classification

Light Multi-segment Activation for Model Compression

2 code implementations16 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.

Knowledge Distillation Model Compression +1

Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States

no code implementations6 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).


TabNN: A Universal Neural Network Solution for Tabular Data

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.

Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome

no code implementations28 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.

CSWA: Aggregation-Free Spatial-Temporal Community Sensing

no code implementations15 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}.

Compressive Sensing Distributed Optimization

Slim-DP: A Light Communication Data Parallelism for DNN

no code implementations27 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.

Dual Supervised Learning

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.

General Classification Image Classification +6

FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification

no code implementations25 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.

Classification General Classification

Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks

no code implementations2 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.

Model Compression

Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding

no code implementations29 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.


WordRep: A Benchmark for Research on Learning Word Representations

no code implementations7 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.

Word Embeddings

KNET: A General Framework for Learning Word Embedding using Morphological Knowledge

no code implementations7 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.

Information Retrieval Retrieval +2

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