Search Results for author: Chao Zhang

Found 220 papers, 77 papers with code

Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

1 code implementation ACL 2022 Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.

AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models

1 code implementation NAACL 2022 Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang

We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training.

Active Learning Pretrained Language Models +2

Transferring SLU Models in Novel Domains

no code implementations ICLR 2019 Yaohua Tang, Kaixiang Mo, Qian Xu, Chao Zhang, Qiang Yang

When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.

Intent Recognition Meta-Learning +4

Neighborhood-Regularized Self-Training for Learning with Few Labels

1 code implementation10 Jan 2023 ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, Carl Yang

Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.

AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data

no code implementations2 Dec 2022 Hao Wang, Lixue Liu, Xueguan Song, Chao Zhang, DaCheng Tao

In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e. g.} surface settlement and landslide) and improve the efficiency of construction.

Active Learning

End-to-End Stochastic Optimization with Energy-Based Model

no code implementations25 Nov 2022 Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.

Scheduling Stochastic Optimization

Single-channel EEG completion using Cascade Transformer

no code implementations16 Nov 2022 Chao Zhang, Siqi Han, Milin Zhang

It is easy for the electroencephalogram (EEG) signal to be incomplete due to packet loss, electrode falling off, etc.

EEG

Goal-Oriented Communications for the IoT and Application to Data Compression

no code implementations10 Nov 2022 Chao Zhang, Hang Zou, Samson Lasaulce, Walid Saad, Marios Kountouris, Mehdi Bennis

Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications.

Data Compression

Distribution-based Emotion Recognition in Conversation

1 code implementation9 Nov 2022 Wen Wu, Chao Zhang, Philip C. Woodland

Automatic emotion recognition in conversation (ERC) is crucial for emotion-aware conversational artificial intelligence.

Emotion Recognition in Conversation

Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems

no code implementations1 Nov 2022 Shaan Bijwadia, Shuo-Yiin Chang, Bo Li, Tara Sainath, Chao Zhang, Yanzhang He

In this work, we propose a method to jointly train the ASR and EP tasks in a single end-to-end (E2E) multitask model, improving EP quality by optionally leveraging information from the ASR audio encoder.

Automatic Speech Recognition speech-recognition

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

1 code implementation1 Nov 2022 Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.

Time Series

Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples

no code implementations31 Oct 2022 Jianjian Qin, Chunzhi Gu, Jun Yu, Chao Zhang

Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism.

3D Anomaly Detection Transfer Learning

End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator

no code implementations29 Oct 2022 Guangzhi Sun, Chao Zhang, Philip C. Woodland

Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model component, is studied, which leverages a slot shortlist with corresponding entities to extract biasing lists.

intent-classification Intent Classification +6

RoChBert: Towards Robust BERT Fine-tuning for Chinese

1 code implementation28 Oct 2022 Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.

Data Augmentation Language Modelling

COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning

1 code implementation27 Oct 2022 Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang, Arnold Overwijk

We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the generalization ability of dense retrieval by combating the distribution shifts between source training tasks and target scenarios.

Language Modelling Retrieval +2

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification

no code implementations26 Oct 2022 Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov

It achieves within 0. 1% accuracy from the highest-performing multi-class baseline, while saving close to 20X on spatio-temporal cost of inference and earlier (3. 5hrs) disease onset prediction.

Image Classification

Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition

no code implementations26 Oct 2022 Wei Wang, Chao Zhang, Xiaopei Wu

In this paper, we make use of limited code-switching data as driving materials and explore a shortcut to quickly develop intra-sentential code-switching recognition skill on the commissioned native language acoustic model, where we propose a data-driven method to make the seed lexicon which is used to train grapheme-to-phoneme model to predict mapping pronunciations for foreign language word in code-switching sentences.

speech-recognition Speech Recognition

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

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

no code implementations3 Oct 2022 Weicong Liang, Yuhui Yuan, Henghui Ding, Xiao Luo, WeiHong Lin, Ding Jia, Zheng Zhang, Chao Zhang, Han Hu

Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.

Depth Estimation Image Classification +4

Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation

no code implementations30 Sep 2022 Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, Vincent Poor

The task is modeled by the minimization problem of a general goal function $f(x;g)$ for which the decision $x$ has to be taken from a quantized version of the parameters $g$.

Quantization

Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach

1 code implementation15 Sep 2022 Yue Yu, Rongzhi Zhang, ran Xu, Jieyu Zhang, Jiaming Shen, Chao Zhang

We propose PATRON, a new method that uses prompt-based uncertainty estimation for data selection for pre-trained language model fine-tuning under cold-start scenarios, i. e., no initial labeled data are available.

Language Modelling Text Classification

Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites

no code implementations15 Sep 2022 Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao

The model subsequently calculates session representations by combining the contextual information with the instant search query using an aggregation network.

Graph Attention

Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification

no code implementations13 Sep 2022 Chao Zhang, Bo Li, Tara Sainath, Trevor Strohman, Sepand Mavandadi, Shuo-Yiin Chang, Parisa Haghani

Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task.

Automatic Speech Recognition Language Identification +1

SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG

no code implementations3 Sep 2022 Chao Zhang, Zijian Tang, Taoming Guo, Jiaxin Lei, Jiaxin Xiao, Anhe Wang, Shuo Bai, Milin Zhang

This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG).

EEG Model Compression +1

Turn-Taking Prediction for Natural Conversational Speech

no code implementations29 Aug 2022 Shuo-Yiin Chang, Bo Li, Tara N. Sainath, Chao Zhang, Trevor Strohman, Qiao Liang, Yanzhang He

This makes doing speech recognition with conversational speech, including one with multiple queries, a challenging task.

speech-recognition Speech Recognition

SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling

1 code implementation7 Aug 2022 Mengyang Liu, Haozheng Luo, Leonard Thong, Yinghao Li, Chao Zhang, Le Song

Compared to frequently used text annotation tools, our annotation tool allows for the development of weak labels in addition to providing a manual annotation experience.

Denoising named-entity-recognition +2

DETRs with Hybrid Matching

1 code implementation26 Jul 2022 Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, WeiHong Lin, Lei Sun, Chao Zhang, Han Hu

This end-to-end signature is important for the versatility of DETR, and it has been generalized to a wide range of visual problems, including instance/semantic segmentation, human pose estimation, and point cloud/multi-view-images based detection, etc.

Object Detection Pose Estimation +2

Model-Aware Contrastive Learning: Towards Escaping Uniformity-Tolerance Dilemma in Training

1 code implementation16 Jul 2022 Zizheng Huang, Chao Zhang, Huaxiong Li, Bo wang, Chunlin Chen

It has been identified that the temperature $ \tau $ of CL loss plays an essential role in automatically concentrating on hard negative samples.

Contrastive Learning

Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription

no code implementations8 Jul 2022 Xianrui Zheng, Chao Zhang, Philip C. Woodland

Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2. 0 (W2V2), have become the backbone of many speech tasks.

Action Detection Activity Detection +3

Learning Disentangled Representations for Controllable Human Motion Prediction

no code implementations4 Jul 2022 Chunzhi Gu, Jun Yu, Chao Zhang

Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion.

Human motion prediction Inductive Bias +1

Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction

no code implementations28 Jun 2022 Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang

We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.

Language Modelling Product Recommendation

Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval

no code implementations20 Jun 2022 Guile Wu, Chao Zhang, Stephan Liwicki

In global consistent quantization, we employ contrastive learning for both embedding and quantized representations and fuses these representations for consistent contrastive regularization between instances.

Contrastive Learning Image Retrieval +2

PROFHIT: Probabilistic Robust Forecasting for Hierarchical Time-series

1 code implementation16 Jun 2022 Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash

We close both these gaps and propose PROFHIT, which is a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.

Time Series Forecasting

Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition

1 code implementation27 May 2022 Yinghao Li, Le Song, Chao Zhang

Weakly supervised named entity recognition methods train label models to aggregate the token annotations of multiple noisy labeling functions (LFs) without seeing any manually annotated labels.

Named Entity Recognition Weakly-Supervised Named Entity Recognition

Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator

no code implementations18 May 2022 Guangzhi Sun, Chao Zhang, Philip C Woodland

MBWE and BLMD further improved the effectiveness of TCPGen and achieved more significant WER reductions on the biasing words.

Dialogue State Tracking Language Modelling +3

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

no code implementations18 May 2022 Wensheng Li, Chao Zhang, Chuncheng Wang, Hanting Guan, DaCheng Tao

Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems.

FlowFormer: A Transformer Architecture for Optical Flow

1 code implementation30 Mar 2022 Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, Hongsheng Li

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow.

Optical Flow Estimation

Learning a Structured Latent Space for Unsupervised Point Cloud Completion

no code implementations CVPR 2022 Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, Hongsheng Li

Specifically, we map a series of related partial point clouds into multiple complete shape and occlusion code pairs and fuse the codes to obtain their representations in the unified latent space.

Point Cloud Completion

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

1 code implementation18 Mar 2022 Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.

Abandoning the Bayer-Filter to See in the Dark

1 code implementation CVPR 2022 Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, Jiajun Shen

Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome raw data.

Low-Light Image Enhancement

Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors

no code implementations8 Mar 2022 Wen Wu, Chao Zhang, Xixin Wu, Philip C. Woodland

In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes.

Emotion Classification Emotion Recognition

Shift-Robust Node Classification via Graph Adversarial Clustering

no code implementations7 Mar 2022 Qi Zhu, Chao Zhang, Chanyoung Park, Carl Yang, Jiawei Han

Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.

Classification Domain Adaptation +1

Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation

no code implementations3 Mar 2022 Rama Cont, Mihai Cucuringu, Renyuan Xu, Chao Zhang

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios.

UA-FedRec: Untargeted Attack on Federated News Recommendation

1 code implementation14 Feb 2022 Jingwei Yi, Fangzhao Wu, Bin Zhu, Yang Yu, Chao Zhang, Guangzhong Sun, Xing Xie

Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.

Federated Learning News Recommendation +2

A Survey on Programmatic Weak Supervision

1 code implementation11 Feb 2022 Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner

Labeling training data has become one of the major roadblocks to using machine learning.

Volatility forecasting with machine learning and intraday commonality

no code implementations8 Feb 2022 Chao Zhang, Yihuang Zhang, Mihai Cucuringu, Zhongmin Qian

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility.

BIG-bench Machine Learning

SIGMA: A Structural Inconsistency Reducing Graph Matching Algorithm

no code implementations6 Feb 2022 Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian

In this paper, we propose a novel criterion to measure the graph matching accuracy, structural inconsistency (SI), which is defined based on the network topological structure.

Graph Matching

Improving the fusion of acoustic and text representations in RNN-T

no code implementations25 Jan 2022 Chao Zhang, Bo Li, Zhiyun Lu, Tara N. Sainath, Shuo-Yiin Chang

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR).

Automatic Speech Recognition speech-recognition

Towards Transferable Unrestricted Adversarial Examples with Minimum Changes

1 code implementation4 Jan 2022 Fangcheng Liu, Chao Zhang, Hongyang Zhang

Extensive experiments verify the effectiveness of our framework on balancing imperceptibility and transferability of the crafted adversarial examples.

Adversarial Attack

Recurring the Transformer for Video Action Recognition

no code implementations CVPR 2022 Jiewen Yang, Xingbo Dong, Liujun Liu, Chao Zhang, Jiajun Shen, Dahai Yu

Besides, the proposed RViT can work on both fixed-length and variant-length video clips properly without requiring large GPU memory thanks to the frame by frame processing flow.

Action Recognition Representation Learning +2

Price Impact of Order Flow Imbalance: Multi-level, Cross-asset and Forecasting

no code implementations25 Dec 2021 Rama Cont, Mihai Cucuringu, Chao Zhang

Second, we examine the notion of cross-impact and show that, once the information from multiple levels is included in OFI, multi-asset models with cross-impact do not provide additional explanatory power for contemporaneous impact compared to a sparse model without cross-impact terms.

Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction

no code implementations17 Dec 2021 Yiyuan She, Jiahui Shen, Chao Zhang

In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning.

Dimensionality Reduction Variable Selection

DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework

no code implementations2 Dec 2021 Chao Zhang, Zhijian Li, Hui Qian, Xin Du

We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously.

Variational Inference

HRFormer: High-Resolution Vision Transformer for Dense Predict

2 code implementations NeurIPS 2021 Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang

We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.

Pose Estimation Semantic Segmentation

PMSSC: Parallelizable Multi-Subset based Self-Expressive Model for Subspace Clustering

no code implementations24 Nov 2021 Katsuya Hotta, Takuya Akashi, Shogo Tokai, Chao Zhang

Subspace clustering methods embrace a self-expressive model that represents each data point as a linear combination of other data points in the dataset are powerful unsupervised learning techniques.

A Universal End-to-End Approach to Portfolio Optimization via Deep Learning

no code implementations17 Nov 2021 Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren

The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.

Portfolio Optimization

Diversity-Promoting Human Motion Interpolation via Conditional Variational Auto-Encoder

no code implementations12 Nov 2021 Chunzhi Gu, Shuofeng Zhao, Chao Zhang

In this paper, we present a deep generative model based method to generate diverse human motion interpolation results.

Motion Interpolation

Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer

no code implementations12 Nov 2021 Jaswanth Yella, Chao Zhang, Sergei Petrov, Yu Huang, Xiaoye Qian, Ali A. Minai, Sthitie Bom

Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing.

GraSSNet: Graph Soft Sensing Neural Networks

no code implementations12 Nov 2021 Yu Huang, Chao Zhang, Jaswanth Yella, Sergei Petrov, Xiaoye Qian, Yufei Tang, Xingquan Zhu, Sthitie Bom

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.

Time Series Classification

Approximating Optimal Transport via Low-rank and Sparse Factorization

no code implementations12 Nov 2021 Weijie Liu, Chao Zhang, Nenggan Zheng, Hui Qian

Optimal transport (OT) naturally arises in a wide range of machine learning applications but may often become the computational bottleneck.

Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word

1 code implementation10 Nov 2021 Chao Zhang, Jaswanth Yella, Yu Huang, Xiaoye Qian, Sergei Petrov, Andrey Rzhetsky, Sthitie Bom

We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets.

Time Series

Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning

1 code implementation2 Nov 2021 Wenyu Zhu, Zhiyao Feng, Zihan Zhang, Jianjun Chen, Zhijian Ou, Min Yang, Chao Zhang

Recovering binary programs' call graphs is crucial for inter-procedural analysis tasks and applications based on them. transfer One of the core challenges is recognizing targets of indirect calls (i. e., indirect callees).

Contrastive Learning Question Answering +1

Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas

no code implementations28 Oct 2021 Chao Zhang, Hanxin Zhang, Atif Khan, Ted Kim, Olasubomi Omoleye, Oluwamayomikun Abiona, Amy Lehman, Christopher O. Olopade, Olufunmilayo I. Olopade, Pedro Lopes, Andrey Rzhetsky

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections.

HRFormer: High-Resolution Transformer for Dense Prediction

1 code implementation18 Oct 2021 Yuhui Yuan, Rao Fu, Lang Huang, WeiHong Lin, Chao Zhang, Xilin Chen, Jingdong Wang

We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that produces low-resolution representations and has high memory and computational cost.

Image Classification Multi-Person Pose Estimation +1

CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

1 code implementation15 Sep 2021 Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash

We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.

Decision Making Probabilistic Time Series Forecasting

Self-Training with Differentiable Teacher

no code implementations Findings (NAACL) 2022 Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha

In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels.

named-entity-recognition Named Entity Recognition +1

Learning to Predict Diverse Human Motions from a Single Image via Mixture Density Networks

no code implementations13 Sep 2021 Chunzhi Gu, Yan Zhao, Chao Zhang

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input.

Human motion prediction motion prediction

Training Algorithm Matters for the Performance of Neural Network Potential: A Case Study of Adam and the Kalman Filter Optimizers

no code implementations8 Sep 2021 Yunqi Shao, Florian M. Dietrich, Carl Nettelblad, Chao Zhang

Here we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the Extended Kalman Filter algorithm (EKF), using the Behler-Parrinello neural network (BPNN) and two publicly accessible datasets of liquid water [Proc.

IEEE BigData 2021 Cup: Soft Sensing at Scale

no code implementations7 Sep 2021 Sergei Petrov, Chao Zhang, Jaswanth Yella, Yu Huang, Xiaoye Qian, Sthitie Bom

The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques.

Association

Auto-encoder based Model for High-dimensional Imbalanced Industrial Data

no code implementations4 Aug 2021 Chao Zhang, Sthitie Bom

However, the successful applications of deep learning in soft sensing are still not widely integrated in factory control systems, because most of the research on soft sensing do not have access to large scale industrial data which are varied, noisy and incomplete.

Representation Learning Sensor Modeling

Towards Making Deep Learning-based Vulnerability Detectors Robust

1 code implementation2 Aug 2021 Zhen Li, Jing Tang, Deqing Zou, Qian Chen, Shouhuai Xu, Chao Zhang, Yichen Li, Hai Jin

Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.

Adapting GPT, GPT-2 and BERT Language Models for Speech Recognition

no code implementations29 Jul 2021 Xianrui Zheng, Chao Zhang, Philip C. Woodland

Furthermore, on the AMI corpus, the proposed conversion for language prior probabilities enables BERT to obtain an extra 3% relative WERR, and the combination of BERT, GPT and GPT-2 results in further improvements.

Automatic Speech Recognition speech-recognition

Combining Frame-Synchronous and Label-Synchronous Systems for Speech Recognition

1 code implementation1 Jul 2021 Qiujia Li, Chao Zhang, Philip C. Woodland

Commonly used automatic speech recognition (ASR) systems can be classified into frame-synchronous and label-synchronous categories, based on whether the speech is decoded on a per-frame or per-label basis.

Automatic Speech Recognition speech-recognition

GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

no code implementations24 Jun 2021 Lixue Liu, Chao Zhang, DaCheng Tao

Multi-fidelity data fusion (MDF) methods aims to use massive LF samples and small amounts of HF samples to develop an accurate and efficient model for describing the system with a reasonable computation burden.

Positive-Unlabeled Data Purification in the Wild for Object Detection

no code implementations CVPR 2021 Jianyuan Guo, Kai Han, Han Wu, Chao Zhang, Xinghao Chen, Chunjing Xu, Chang Xu, Yunhe Wang

In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data.

Knowledge Distillation object-detection +1

Towards Transferable Adversarial Perturbations with Minimum Norm

no code implementations ICML Workshop AML 2021 Fangcheng Liu, Chao Zhang, Hongyang Zhang

In this work, we propose a \emph{geometry-aware framework} to generate transferable adversarial perturbation with minimum norm for each input.

Model Selection

Image Deformation Estimation via Multi-Objective Optimization

no code implementations8 Jun 2021 Takumi Nakane, Haoran Xie, Chao Zhang

Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs).

When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

1 code implementation NeurIPS 2021 Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, B. Aditya Prakash

We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value.

Time Series Forecasting

Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling

no code implementations2 Jun 2021 Chao Zhang, Samson Lasaulce, Martin Hennebel, Lucas Saludjian, Patrick Panciatici, H. Vincent Poor

For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions.

Decision Making Management +1

Efficient Cross-Device Federated Learning Algorithms for Minimax Problems

no code implementations29 May 2021 Jiahao Xie, Chao Zhang, Zebang Shen, Weijie Liu, Hui Qian

In many machine learning applications where massive and privacy-sensitive data are generated on numerous mobile or IoT devices, collecting data in a centralized location may be prohibitive.

Federated Learning

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

2 code implementations ACL 2021 Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song

To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.

named-entity-recognition NER +1

Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model

1 code implementation8 May 2021 Lingwei Peng, Hui Qian, Zhebang Shen, Chao Zhang, Fei Li

Model-free deep reinforcement learning has achieved great success in many domains, such as video games, recommendation systems and robotic control tasks.

Continuous Control Recommendation Systems

DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning

no code implementations23 Apr 2021 Sheldon Fung, Xuequan Lu, Chao Zhang, Chang-Tsun Li

Extensive experiments show that our unsupervised learning method enables comparable detection performance to state-of-the-art supervised techniques, in both the intra- and inter-dataset settings.

Contrastive Learning DeepFake Detection +1

Sketch-based Normal Map Generation with Geometric Sampling

no code implementations23 Apr 2021 Yi He, Haoran Xie, Chao Zhang, Xi Yang, Kazunori Miyata

This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling.

Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods

no code implementations12 Apr 2021 Xianjie Gao, Xueguan Song, Maolin Shi, Chao Zhang, Hongwei Zhang

In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters, which can instantaneously provide the future values of the TBM load parameters as long as the current data are collected.

BIG-bench Machine Learning

A Riemannian smoothing steepest descent method for non-Lipschitz optimization on submanifolds

no code implementations9 Apr 2021 Chao Zhang, Xiaojun Chen, Shiqian Ma

In this paper, we propose a Riemannian smoothing steepest descent method to minimize a nonconvex and non-Lipschitz function on submanifolds.

Semantic Scene Completion via Integrating Instances and Scene in-the-Loop

1 code implementation CVPR 2021 Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, Hongsheng Li

The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.

3D Semantic Scene Completion Scene Understanding

A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training

no code implementations12 Mar 2021 Adnan Haider, Chao Zhang, Florian L. Kreyssig, Philip C. Woodland

This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner.

Automatic Speech Recognition Second-order methods +1

Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions

no code implementations10 Mar 2021 Chao Zhang, Shihan Wang, Henk Aarts, Mehdi Dastani

Reinforcement learning (RL) agents in human-computer interactions applications require repeated user interactions before they can perform well.

reinforcement-learning reinforcement Learning

Multi-cell NOMA: Coherent Reconfigurable Intelligent Surfaces Model With Stochastic Geometry

no code implementations3 Mar 2021 Chao Zhang, Wenqiang Yi, Yuanwei Liu, Qiang Wang

Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.

Information Theory Information Theory

Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning

2 code implementations21 Jan 2021 Lang Huang, Chao Zhang, Hongyang Zhang

We propose self-adaptive training -- a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost -- to advance both supervised and self-supervised learning of deep neural networks.

Representation Learning Self-Supervised Learning

Theory-based Habit Modeling for Enhancing Behavior Prediction

no code implementations5 Jan 2021 Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniel Lakens, Boris de Ruyter, Wijnand A. IJsselsteijn

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment.

Partial Gromov-Wasserstein Learning for Partial Graph Matching

no code implementations2 Dec 2020 Weijie Liu, Chao Zhang, Jiahao Xie, Zebang Shen, Hui Qian, Nenggan Zheng

Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.

Graph Matching

Deep Discriminative Feature Learning for Accent Recognition

1 code implementation25 Nov 2020 Wei Wang, Chao Zhang, Xiaopei Wu

Accent recognition with deep learning framework is a similar work to deep speaker identification, they're both expected to give the input speech an identifiable representation.

Face Recognition Speaker Identification +3

Improving Prosody Modelling with Cross-Utterance BERT Embeddings for End-to-end Speech Synthesis

no code implementations6 Nov 2020 Guanghui Xu, Wei Song, Zhengchen Zhang, Chao Zhang, Xiaodong He, BoWen Zhou

Despite prosody is related to the linguistic information up to the discourse structure, most text-to-speech (TTS) systems only take into account that within each sentence, which makes it challenging when converting a paragraph of texts into natural and expressive speech.

Sentence Embeddings Speech Synthesis

Emotion recognition by fusing time synchronous and time asynchronous representations

no code implementations27 Oct 2020 Wen Wu, Chao Zhang, Philip C. Woodland

In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB).

Automatic Speech Recognition Classification +4

Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

1 code implementation EMNLP 2020 Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang

Fine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization.

Language Modelling OOD Detection +2

Combination of Deep Speaker Embeddings for Diarisation

no code implementations22 Oct 2020 Guangzhi Sun, Chao Zhang, Phil Woodland

Significant progress has recently been made in speaker diarisation after the introduction of d-vectors as speaker embeddings extracted from neural network (NN) speaker classifiers for clustering speech segments.

Action Detection Activity Detection +1

Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction

no code implementations21 Oct 2020 Rui Feng, Jie Yuan, Chao Zhang

We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres. We propose a reading comprehension framework for event extraction. Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem.

Event Detection Event Extraction +2

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

Text Classification Using Label Names Only: A Language Model Self-Training Approach

2 code implementations EMNLP 2020 Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han

In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents.

Classification Document Classification +6

SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

1 code implementation EMNLP 2020 Rongzhi Zhang, Yue Yu, Chao Zhang

Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.

Active Learning Data Augmentation +3

Transformer-Based Neural Text Generation with Syntactic Guidance

1 code implementation5 Oct 2020 Yinghao Li, Rui Feng, Isaac Rehg, Chao Zhang

We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation.

Text Generation

A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling

1 code implementation5 Oct 2020 Wanzheng Zhu, Chao Zhang, Shuochao Yao, Xiaobin Gao, Jiawei Han

We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling.

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization

1 code implementation4 Oct 2020 Yue Yu, Kexin Huang, Chao Zhang, Lucas M. Glass, Jimeng Sun, Cao Xiao

Furthermore, most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is a more meaningful but harder task.

Data Integration Knowledge Graphs

Exploring the Hierarchy in Relation Labels for Scene Graph Generation

no code implementations12 Sep 2020 Yi Zhou, Shuyang Sun, Chao Zhang, Yikang Li, Wanli Ouyang

By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem.

Graph Generation Scene Graph Generation

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

1 code implementation NeurIPS 2021 Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.

Knowledge Graphs Transfer Learning

Example-based Color Transfer with Gaussian Mixture Modeling

no code implementations31 Aug 2020 Chunzhi Gu, Xuequan Lu, Chao Zhang

In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids.

SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates

2 code implementations ICML 2020 Lingkai Kong, Jimeng Sun, Chao Zhang

We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective.

A variational quantum algorithm for Hamiltonian diagonalization

no code implementations22 Aug 2020 Jinfeng Zeng, Chenfeng Cao, Chao Zhang, Pengxiang Xu, Bei Zeng

To obtain the full spectrum of the Hamiltonian, we use a quantum imaginary time evolution algorithm with high temperature, which prepares a thermal state with a small correlation length.

Quantum Physics

Neural Kalman Filtering for Speech Enhancement

no code implementations28 Jul 2020 Wei Xue, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, BoWen Zhou

Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven.

Automatic Speech Recognition Speech Enhancement +1

Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding

1 code implementation18 Jul 2020 Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Chao Zhang, Jiawei Han

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.

text-classification Topic Models

Deep Patch-based Human Segmentation

no code implementations11 Jul 2020 Dongbo Zhang, Zheng Fang, Xuequan Lu, Hong Qin, Antonio Robles-Kelly, Chao Zhang, Ying He

3D human segmentation has seen noticeable progress in re-cent years.

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

1 code implementation18 Jun 2020 Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun, Chao Zhang

We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion.

Taxonomy Expansion

Curating a COVID-19 data repository and forecasting county-level death counts in the United States

1 code implementation16 May 2020 Nick Altieri, Rebecca L. Barter, James Duncan, Raaz Dwivedi, Karl Kumbier, Xiao Li, Robert Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan, Tiffany Tang, Yu Wang, Chao Zhang, Bin Yu

We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.

COVID-19 Tracking Decision Making +2

Partially-Typed NER Datasets Integration: Connecting Practice to Theory

no code implementations1 May 2020 Shi Zhi, Liyuan Liu, Yu Zhang, Shiyin Wang, Qi Li, Chao Zhang, Jiawei Han

While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them.

named-entity-recognition NER

paper2repo: GitHub Repository Recommendation for Academic Papers

no code implementations13 Apr 2020 Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek Abdelzaher

Motivated by this trend, we describe a novel item-item cross-platform recommender system, $\textit{paper2repo}$, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.

Recommendation Systems

Spherically symmetric static black holes in Einstein-aether theory

no code implementations13 Apr 2020 Chao Zhang, Xiang Zhao, Kai Lin, Shaojun Zhang, Wen Zhao, Anzhong Wang

In particular, we find that, out of the five non-trivial field equations, only three are independent, so the problem is well-posed, as now generically there are only three unknown functions, {$F(r), B(r), A(r)$, where $F$ and $B$ are metric coefficients, and $A$ describes the aether field.}

General Relativity and Quantum Cosmology Astrophysics of Galaxies High Energy Physics - Phenomenology High Energy Physics - Theory

SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm

no code implementations30 Mar 2020 Takumi Nakane, Xuequan Lu, Chao Zhang

In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history.

Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection

1 code implementation CVPR 2020 Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, Chang Xu

To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.

Image Classification Neural Architecture Search +2

Self-Adaptive Training: beyond Empirical Risk Minimization

3 code implementations NeurIPS 2020 Lang Huang, Chao Zhang, Hongyang Zhang

We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost---to improve generalization of deep learning for potentially corrupted training data.

General Classification

G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data

no code implementations20 Jan 2020 Chao Zhang, Xuequan Lu, Katsuya Hotta, Xi Yang

The WA data can be naturally obtained in an interactive way for specific tasks, for example, in the case of homography estimation, one can easily annotate points on the same plane/object with a single label by observing the image.

Homography Estimation

Improved Large-margin Softmax Loss for Speaker Diarisation

no code implementations10 Nov 2019 Yassir Fathullah, Chao Zhang, Philip C. Woodland

Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers.

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

no code implementations10 Nov 2019 Chao Zhang, Zichao Yang, Xiaodong He, Li Deng

This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.

Multimodal Deep Learning Question Answering +6

Spherical Text Embedding

1 code implementation NeurIPS 2019 Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han

While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding.

Riemannian optimization Word Similarity

Discriminative Neural Clustering for Speaker Diarisation

1 code implementation22 Oct 2019 Qiujia Li, Florian L. Kreyssig, Chao Zhang, Philip C. Woodland

In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem.

Data Augmentation

Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification

1 code implementation ICCV 2019 Jianyuan Guo, Yuhui Yuan, Lang Huang, Chao Zhang, Jinge Yao, Kai Han

On the other hand, there still exist many useful contextual cues that do not fall into the scope of predefined human parts or attributes.

Human Parsing Person Re-Identification

Efficient Projection-Free Online Methods with Stochastic Recursive Gradient

no code implementations21 Oct 2019 Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, Hui Qian

This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems.

Aggregated Gradient Langevin Dynamics

no code implementations21 Oct 2019 Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian

In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.

FUSE: Multi-Faceted Set Expansion by Coherent Clustering of Skip-grams

1 code implementation10 Oct 2019 Wanzheng Zhu, Hongyu Gong, Jiaming Shen, Chao Zhang, Jingbo Shang, Suma Bhat, Jiawei Han

In this paper, we study the task of multi-faceted set expansion, which aims to capture all semantic facets in the seed set and return multiple sets of entities, one for each semantic facet.

Language Modelling

On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications

no code implementations8 Oct 2019 Chao Zhang, Min-Hsiu Hsieh, DaCheng Tao

We also develop the tail inequalities for matrix random series and matrix martingale difference sequence.

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

2 code implementations8 Oct 2019 Yunqi Shao, Matti Hellström, Pavlin D. Mitev, Lisanne Knijff, Chao Zhang

Atomic neural networks (ANNs) constitute a class of machine learning methods for predicting potential energy surfaces and physico-chemical properties of molecules and materials.

Computational Physics Disordered Systems and Neural Networks Chemical Physics

Device-independent verification of Einstein-Podolsky-Rosen steering

no code implementations30 Sep 2019 Yuan-Yuan Zhao, Chao Zhang, Shuming Cheng, Xinhui Li, Yu Guo, Bi-Heng Liu, Huan-Yu Ku, Shin-Liang Chen, Qiaoyan Wen, Yun-Feng Huang, Guo-Yong Xiang, Chuan-Feng Li, Guang-Can Guo

If entanglement could be verified without any trust in the devices of observers, i. e., in a device-independent (DI) way, then unconditional security can be guaranteed for various quantum information tasks.

Quantum Physics

Decision Set Optimization and Energy-Efficient MIMO Communications

no code implementations16 Sep 2019 Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, Patrick Panciatici

We propose a framework to find a good (finite) decision set which induces a minimal performance loss w. r. t.

Integrating Source-channel and Attention-based Sequence-to-sequence Models for Speech Recognition

no code implementations14 Sep 2019 Qiujia Li, Chao Zhang, Philip C. Woodland

This paper proposes a novel automatic speech recognition (ASR) framework called Integrated Source-Channel and Attention (ISCA) that combines the advantages of traditional systems based on the noisy source-channel model (SC) and end-to-end style systems using attention-based sequence-to-sequence models.

Automatic Speech Recognition speech-recognition

Discriminative Topic Mining via Category-Name Guided Text Embedding

1 code implementation20 Aug 2019 Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Yu Zhang, Jiawei Han

We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.

Document Classification General Classification +3

Orientation-aware Semantic Segmentation on Icosahedron Spheres

1 code implementation ICCV 2019 Chao Zhang, Stephan Liwicki, William Smith, Roberto Cipolla

For the spherical domain, several methods recently adopt an icosahedron mesh, but systems are typically rotation invariant or require significant memory and parameters, thus enabling execution only at very low resolutions.

Autonomous Driving Semantic Segmentation

Multi-scale Template Matching with Scalable Diversity Similarity in an Unconstrained Environment

no code implementations2 Jul 2019 Yi Zhang, Chao Zhang, Takuya Akashi

We propose a novel multi-scale template matching method which is robust against both scaling and rotation in unconstrained environments.

Template Matching

Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction

no code implementations23 Jun 2019 Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, Le Song

In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.

Combinatorial Optimization

Multi-Span Acoustic Modelling using Raw Waveform Signals

no code implementations21 Jun 2019 Patrick von Platen, Chao Zhang, Philip Woodland

This paper proposes a novel multi-span structure for acoustic modelling based on the raw waveform with multiple streams of CNN input layers, each processing a different span of the raw waveform signal.

Acoustic Modelling Automatic Speech Recognition +1

ADA-Tucker: Compressing Deep Neural Networks via Adaptive Dimension Adjustment Tucker Decomposition

no code implementations18 Jun 2019 Zhisheng Zhong, Fangyin Wei, Zhouchen Lin, Chao Zhang

Furthermore, we propose that weight tensors in networks with proper order and balanced dimension are easier to be compressed.

A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

no code implementations8 Jun 2019 Yu-cheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng

This paper provides a simple procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs).

Fragmentation of shells: An analogy with the crack formation in tree bark

no code implementations6 Jun 2019 Chuang-Shi Shen, Chao Zhang, Xiaosheng Gao, Yulong Li

We recognize that the fragmentation problem in shells is analogous to the cracking behavior of tree bark, and closed form solutions is obtained to describe the relationship between the expansion velocity and the number of necks with consideration of the strain rate dependent strength of the shell material.

Soft Condensed Matter Applied Physics

Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation

no code implementations17 May 2019 Hang Zou, Chao Zhang, Samson Lasaulce, Lucas Saludjian, Patrick Panciatici

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task.

FrameRank: A Text Processing Approach to Video Summarization

no code implementations11 Apr 2019 Zhuo Lei, Chao Zhang, Qian Zhang, Guoping Qiu

In constructing the dataset, because of the subjectivity of user-generated video summarization, we manually annotate 25 summaries for each video, which are in total 1300 summaries.

Unsupervised Video Summarization