Search Results for author: Ying Zhang

Found 70 papers, 26 papers with code

A Language Model-based Generative Classifier for Sentence-level Discourse Parsing

no code implementations EMNLP 2021 Ying Zhang, Hidetaka Kamigaito, Manabu Okumura

Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence.

Discourse Segmentation Language Modelling

Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving

no code implementations1 May 2022 Naifan Li, Fan Song, Ying Zhang, Pengpeng Liang, Erkang Cheng

In this work, we propose a systematic study on simple Copy-Paste data augmentation for rare object detection in autonomous driving.

Autonomous Driving Data Augmentation +1

Probabilistic Models for Manufacturing Lead Times

no code implementations28 Apr 2022 Recep Yusuf Bekci, Yacine Mahdid, Jinling Xing, Nikita Letov, Ying Zhang, Zahid Pasha

In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes.

Gaussian Processes

Influence of the Endothelial Surface Layer on the Wall-induced Migration of Red Blood Cells

1 code implementation23 Mar 2022 Ying Zhang, Thomas G. Fai

The endothelial lining of blood vessels presents a large surface area for exchanging materials between blood and tissues.

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

no code implementations2 Feb 2022 Xi Yang, 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

We developed GatorTron models from scratch using the BERT architecture of different sizes including 345 million, 3. 9 billion, and 8. 9 billion parameters, compared GatorTron with three existing transformer models in the clinical and biomedical domain on 5 different clinical NLP tasks including clinical concept extraction, relation extraction, semantic textual similarity, natural language inference, and medical question answering, to examine how large transformer models could help clinical NLP at different linguistic levels.

Clinical Concept Extraction Language Modelling +4

Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

no code implementations25 Jan 2022 Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang, Xuemin Lin

In recent years, many advanced techniques for query vertex ordering (i. e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules.

reinforcement-learning

Detailed Balance for Particle Models of Reversible Reactions in Bounded Domains

no code implementations11 Jan 2022 Ying Zhang, Samuel A. Isaacson

In bounded domains with no-flux boundary conditions, when choosing unbinding kernels consistent with several commonly used binding kernels, we show that preserving detailed balance of spatial reaction-fluxes at all points requires spatially varying unbinding rate functions near the domain boundary.

Building an AI-ready RSE Workforce

no code implementations9 Nov 2021 Ying Zhang, Matthew A. Gitzendanner, Dan S. Maxwell, Justin W. Richardson, Kaleb E. Smith, Eric A. Stubbs, Brian J. Stucky, Jingchao Zhang, Erik Deumens

Artificial Intelligence has been transforming industries and academic research across the globe, and research software development is no exception.

Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems

no code implementations8 Nov 2021 Ying Zhang, Yanbo Chen, Jianhui Wang, Yue Meng, Tianqiao Zhao

Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.

A Survey of Knowledge Enhanced Pre-trained Models

no code implementations1 Oct 2021 Jian Yang, Gang Xiao, Yulong Shen, Wei Jiang, Xinyu Hu, Ying Zhang, Jinghui Peng

Pre-trained models learn contextualized word representations on large-scale text corpus through a self-supervised learning method, which has achieved promising performance after fine-tuning.

Representation Learning Self-Supervised Learning

Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference

no code implementations16 Sep 2021 Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Yifang Yin, Ying Zhang, Roger Zimmermann

Sensors are the key to sensing the environment and imparting benefits to smart cities in many aspects, such as providing real-time air quality information throughout an urban area.

Graph Attention

Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders

no code implementations ICCV 2021 Jing Li, Di Kang, Wenjie Pei, Xuefei Zhe, Ying Zhang, Zhenyu He, Linchao Bao

In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.

Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition

1 code implementation Findings (ACL) 2021 Ying Zhang, Fandong Meng, Yufeng Chen, Jinan Xu, Jie zhou

In this paper, we tackle the problem by transferring knowledge from three aspects, i. e., domain, language and task, and strengthening connections among them.

Named Entity Recognition NER +1

Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function

1 code implementation19 Jul 2021 Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, Ying Zhang

We consider non-convex stochastic optimization problems where the objective functions have super-linearly growing and discontinuous stochastic gradients.

Stochastic Optimization Transfer Learning

Animatable Neural Radiance Fields from Monocular RGB Videos

1 code implementation25 Jun 2021 Jianchuan Chen, Ying Zhang, Di Kang, Xuefei Zhe, Linchao Bao, Xu Jia, Huchuan Lu

We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.

3D Human Reconstruction Frame +3

Model-based 3D Hand Reconstruction via Self-Supervised Learning

1 code implementation CVPR 2021 Yujin Chen, Zhigang Tu, Di Kang, Linchao Bao, Ying Zhang, Xuefei Zhe, Ruizhi Chen, Junsong Yuan

For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.

Self-Supervised Learning

Quantifying measurement-induced nonbilocal correlation

no code implementations9 Mar 2021 Ying Zhang, Kan He

In the paper, we devote to defining an available measure to quantify the nonbilocal correlation in the entanglement-swapping experiment.

Quantum Physics

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

no code implementations25 Feb 2021 Ying Zhang, Meng Yue, Jianhui Wang

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies.

reinforcement-learning

Similarity Reasoning and Filtration for Image-Text Matching

1 code implementation5 Jan 2021 Haiwen Diao, Ying Zhang, Lin Ma, Huchuan Lu

Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words.

Cross-Modal Retrieval Image Retrieval +1

A Unified Spectral Sparsification Framework for Directed Graphs

no code implementations1 Jan 2021 Ying Zhang, Zhiqiang Zhao, Zhuo Feng

For the first time, we prove the existence of linear-sized spectral sparsifiers for general directed graphs and introduce a practically-efficient and unified spectral graph sparsification approach that allows sparsifying real-world, large-scale directed and undirected graphs with guaranteed preservation of the original graph spectra.

Existence and uniqueness of local weak solution of d-dimensional tropical climate model without thermal diffusion in inhomogeneous Besov space

no code implementations24 Dec 2020 Baoquan Yuan, Ying Zhang

This paper studies the existence and uniqueness of local weak solutions to the d-dimensional tropical climate model without thermal diffusion.

Analysis of PDEs

Emotional Conversation Generation with Heterogeneous Graph Neural Network

1 code implementation9 Dec 2020 Yunlong Liang, Fandong Meng, Ying Zhang, Jinan Xu, Yufeng Chen, Jie zhou

Firstly, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i. e., the dialogue history, its emotion flow, facial expressions, audio, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback.

SF-GRASS: Solver-Free Graph Spectral Sparsification

no code implementations17 Aug 2020 Ying Zhang, Zhiqiang Zhao, Zhuo Feng

Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc.

Representation Learning

Consensus-Aware Visual-Semantic Embedding for Image-Text Matching

1 code implementation ECCV 2020 Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, Lin Ma

In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.

Image Captioning Text Matching

A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating

no code implementations2 Jul 2020 Sotirios Sabanis, Ying Zhang

We are thus able to provide theoretical guarantees for the algorithm's convergence in (standard) Wasserstein distances for both convex and non-convex objective functions.

Stochastic Optimization

GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions

1 code implementation12 May 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xuemin Lin

We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model.

Binarized Graph Neural Network

no code implementations19 Apr 2020 Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, Xiangjian He, Yiguang Lin, Xuemin Lin

Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.

Graph Embedding

Machine learning on DNA-encoded libraries: A new paradigm for hit-finding

no code implementations31 Jan 2020 Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A. Clark, John W. Cuozzo, Marie-Aude Guié, John P. Guilinger, Christelle Huguet, Christopher D. Hupp, Anthony D. Keefe, Christopher J. Mulhern, Ying Zhang, Patrick Riley

We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds.

Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization

no code implementations4 Oct 2019 Ying Zhang, Ömer Deniz Akyildiz, Theodoros Damoulas, Sotirios Sabanis

Within the context of empirical risk minimization, see Raginsky, Rakhlin, and Telgarsky (2017), we are concerned with a non-asymptotic analysis of sampling algorithms used in optimization.

Bayesian Inference Variational Inference

Retrieving Signals in the Frequency Domain with Deep Complex Extractors

1 code implementation25 Sep 2019 Chiheb Trabelsi, Olexa Bilaniuk, Ousmane Dia, Ying Zhang, Mirco Ravanelli, Jonathan Binas, Negar Rostamzadeh, Christopher J Pal

Using the Wall Street Journal Dataset, we compare our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.

Audio Source Separation

Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

no code implementations IJCNLP 2019 Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Ying Zhang

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans.

Improving Captioning for Low-Resource Languages by Cycle Consistency

no code implementations21 Aug 2019 Yike Wu, Shiwan Zhao, Jia Chen, Ying Zhang, Xiaojie Yuan, Zhong Su

Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.

Translation

A Survey and Experimental Analysis of Distributed Subgraph Matching

1 code implementation27 Jun 2019 Longbin Lai, Zhu Qing, Zhengyi Yang, Xin Jin, Zhengmin Lai, Ran Wang, Kongzhang Hao, Xuemin Lin, Lu Qin, Wenjie Zhang, Ying Zhang, Zhengping Qian, Jingren Zhou

We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.

Databases

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case

no code implementations30 May 2019 Ngoc Huy Chau, Éric Moulines, Miklos Rásonyi, Sotirios Sabanis, Ying Zhang

We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017).

Stochastic Optimization

Voiceprint recognition of Parkinson patients based on deep learning

no code implementations17 Dec 2018 Zhijing Xu, Juan Wang, Ying Zhang, Xiangjian He

In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features.

General Classification

Fashion-Gen: The Generative Fashion Dataset and Challenge

2 code implementations21 Jun 2018 Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal

We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.

Image Generation

Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition

1 code implementation20 Jun 2018 Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato De Mori, Yoshua Bengio

Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models.

Automatic Speech Recognition Frame

Medical Concept Embedding with Time-Aware Attention

1 code implementation6 Jun 2018 Xiangrui Cai, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Ying Zhang, Xiaojie Yuan

Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics.

IDEL: In-Database Entity Linking with Neural Embeddings

no code implementations13 Mar 2018 Torsten Kilias, Alexander Löser, Felix A. Gers, Richard Koopmanschap, Ying Zhang, Martin Kersten

We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities.

Entity Linking

Online Product Quantization

no code implementations29 Nov 2017 Donna Xu, Ivor W. Tsang, Ying Zhang

The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.

Quantization

Device-to-Device Load Balancing for Cellular Networks

1 code implementation7 Oct 2017 Lei Deng, Yinghui He, Ying Zhang, Minghua Chen, Zongpeng Li, Jack Y. B. Lee, Ying Jun Zhang, Lingyang Song

The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency.

Networking and Internet Architecture

Deep Mutual Learning

8 code implementations CVPR 2018 Ying Zhang, Tao Xiang, Timothy M. Hospedales, Huchuan Lu

Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network.

Person Re-Identification

Deep Complex Networks

8 code implementations ICLR 2018 Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J. Pal

Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.

Image Classification Music Transcription

Fully Distributed and Asynchronized Stochastic Gradient Descent for Networked Systems

no code implementations13 Apr 2017 Ying Zhang

This paper considers a general data-fitting problem over a networked system, in which many computing nodes are connected by an undirected graph.

Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

1 code implementation10 Jan 2017 Ying Zhang, Mohammad Pezeshki, Philemon Brakel, Saizheng Zhang, Cesar Laurent Yoshua Bengio, Aaron Courville

Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings.

Automatic Speech Recognition

Recurrent Neural Networks With Limited Numerical Precision

1 code implementation21 Nov 2016 Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, Yoshua Bengio

Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often high.

Quantization

Professor Forcing: A New Algorithm for Training Recurrent Networks

1 code implementation NeurIPS 2016 Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio

We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps.

Domain Adaptation Handwriting generation +2

Approximate Nearest Neighbor Search on High Dimensional Data --- Experiments, Analyses, and Improvement (v1.0)

3 code implementations8 Oct 2016 Wen Li, Ying Zhang, Yifang Sun, Wei Wang, Wenjie Zhang, Xuemin Lin

Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision.

Databases

Recurrent Neural Networks With Limited Numerical Precision

1 code implementation24 Aug 2016 Joachim Ott, Zhouhan Lin, Ying Zhang, Shih-Chii Liu, Yoshua Bengio

We present results from the use of different stochastic and deterministic reduced precision training methods applied to three major RNN types which are then tested on several datasets.

Binarization

On Multiplicative Integration with Recurrent Neural Networks

no code implementations NeurIPS 2016 Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov

We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs).

Language Modelling

Sample-Specific SVM Learning for Person Re-Identification

no code implementations CVPR 2016 Ying Zhang, Baohua Li, Huchuan Lu, Atshushi Irie, Xiang Ruan

Person re-identification addresses the problem of matching people across disjoint camera views and extensive efforts have been made to seek either the robust feature representation or the discriminative matching metrics.

Dictionary Learning imbalanced classification +1

Theano: A Python framework for fast computation of mathematical expressions

1 code implementation9 May 2016 The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

Dimensionality Reduction General Classification

Batch Normalized Recurrent Neural Networks

no code implementations5 Oct 2015 César Laurent, Gabriel Pereyra, Philémon Brakel, Ying Zhang, Yoshua Bengio

Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies.

Speech Recognition

Subspace Clustering by Mixture of Gaussian Regression

no code implementations CVPR 2015 Baohua Li, Ying Zhang, Zhouchen Lin, Huchuan Lu

Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG).

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