Search Results for author: Yue Yu

Found 51 papers, 19 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.

Nonparametric learning of kernels in nonlocal operators

no code implementations23 May 2022 Fei Lu, Qingci An, Yue Yu

In this work, we provide a rigorous identifiability analysis and convergence study for the learning of kernels in nonlocal operators.

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

no code implementations19 May 2022 Yang Xiang, Zhihua Wu, Weibao Gong, Siyu Ding, Xianjie Mo, Yuang Liu, Shuohuan Wang, Peng Liu, Yongshuai Hou, Long Li, Bin Wang, Shaohuai Shi, Yaqian Han, Yue Yu, Ge Li, Yu Sun, Yanjun Ma, dianhai yu

We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.

Cross-Lingual Natural Language Inference Language Modelling +1

Modality-Balanced Embedding for Video Retrieval

no code implementations18 Apr 2022 Xun Wang, Bingqing Ke, Xuanping Li, Fangyu Liu, Mingyu Zhang, Xiao Liang, Qiushi Xiao, Cheng Luo, Yue Yu

This modality imbalanceresults from a) modality gap: the relevance between a query and a video text is much easier to learn as the query is also a piece of text, with the same modality as the video text; b) data bias: most training samples can be solved solely by text matching.

Text Matching Video Retrieval

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

no code implementations1 Apr 2022 Huaiqian You, Quinn Zhang, Colton J. Ross, Chung-Hao Lee, Ming-Chen Hsu, Yue Yu

To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge.

Operator learning

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.

Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling

no code implementations15 Mar 2022 Huaiqian You, Quinn Zhang, Colton J. Ross, Chung-Hao Lee, Yue Yu

In this work, we propose to use data-driven modeling, which directly utilizes high-fidelity simulation and/or experimental measurements to predict a material's response without using conventional constitutive models.

Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems

no code implementations25 Feb 2022 Minglang Yin, Enrui Zhang, Yue Yu, George Em Karniadakis

In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver.

DeepSensor: Deep Learning Testing Framework Based on Neuron Sensitivity

no code implementations12 Feb 2022 Haibo Jin, Ruoxi Chen, Haibin Zheng, Jinyin Chen, Zhenguang Liu, Qi Xuan, Yue Yu, Yao Cheng

Besides, considerable robustness enhancement can be reached when adopting testing examples based on neuron sensitivity for retraining.

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.

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network

no code implementations6 Jan 2022 Huaiqian You, Yue Yu, Marta D'Elia, Tian Gao, Stewart Silling

In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images.

Image Classification

NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

1 code implementation25 Dec 2021 Haibin Zheng, Zhiqing Chen, Tianyu Du, Xuhong Zhang, Yao Cheng, Shouling Ji, Jingyi Wang, Yue Yu, Jinyin Chen

To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data.

Fairness

CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing

no code implementations24 Dec 2021 Haibo Jin, Ruoxi Chen, Jinyin Chen, Yao Cheng, Chong Fu, Ting Wang, Yue Yu, Zhaoyan Ming

Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks.

DNN Testing

NIP: Neuron-level Inverse Perturbation Against Adversarial Attacks

no code implementations24 Dec 2021 Ruoxi Chen, Haibo Jin, Jinyin Chen, Haibin Zheng, Yue Yu, Shouling Ji

From the perspective of image feature space, some of them cannot reach satisfying results due to the shift of features.

A revised comparison between FF five-factor model and three-factor model,based on China's A-share market

no code implementations16 Oct 2021 Zhijing Zhang, Yue Yu, Qinghua Ma, Haixiang Yao

In allusion to some contradicting results in existing research, this paper selects China's latest stock data from 2005 to 2020 for empirical analysis.

WRENCH: A Comprehensive Benchmark for Weak Supervision

1 code implementation23 Sep 2021 Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, Alexander Ratner

To address these problems, we introduce a benchmark platform, WRENCH, for thorough and standardized evaluation of WS approaches.

Self-Training with Differentiable Teacher

no code implementations15 Sep 2021 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 Weakly Supervised Classification

A physics-informed variational DeepONet for predicting the crack path in brittle materials

no code implementations16 Aug 2021 Somdatta Goswami, Minglang Yin, Yue Yu, George Karniadakis

We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis.

Advances in Trajectory Optimization for Space Vehicle Control

1 code implementation5 Aug 2021 Danylo Malyuta, Yue Yu, Purnanand Elango, Behcet Acikmese

Space mission design places a premium on cost and operational efficiency.

A data-driven peridynamic continuum model for upscaling molecular dynamics

no code implementations4 Aug 2021 Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia

Nonlocal models, including peridynamics, often use integral operators that embed lengthscales in their definition.

GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network

no code implementations9 Jul 2021 Zuohui Chen, Renxuan Wang, Jingyang Xiang, Yue Yu, Xin Xia, Shouling Ji, Qi Xuan, Xiaoniu Yang

Deep Neural Networks (DNN) are known to be vulnerable to adversarial samples, the detection of which is crucial for the wide application of these DNN models.

DAGs with No Curl: An Efficient DAG Structure Learning Approach

1 code implementation14 Jun 2021 Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji

To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly.

deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search

1 code implementation24 Mar 2021 Chen Zeng, Yue Yu, Shanshan Li, Xin Xia, Zhiming Wang, Mingyang Geng, Bailin Xiao, Wei Dong, Xiangke Liao

With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language.

Code Search

On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma

no code implementations5 Feb 2021 Yue Yu, Shahriar Talebi, Henk J. van Waarde, Ufuk Topcu, Mehran Mesbahi, Behçet Açıkmeşe

Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold.

An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture

no code implementations5 Jan 2021 Yue Yu, Huaiqian You, Nathaniel Trask

In the absence of fracture, when a corresponding classical continuum mechanics model exists, our improvements provide asymptotically compatible convergence to corresponding local solutions, eliminating surface effects and issues with traction loading which have historically plagued peridynamic discretizations.

Numerical Analysis Computational Engineering, Finance, and Science Numerical Analysis Analysis of PDEs

Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws

no code implementations8 Dec 2020 Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia

We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials.

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

1 code implementation NeurIPS 2020 Dennis Wei, Tian Gao, Yue Yu

This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks.

MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

no code implementations Findings of the Association for Computational Linguistics 2020 Lu Zhang, Mo Yu, Tian Gao, Yue Yu

Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships.

Knowledge Graphs

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 +2

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.

Knowledge Graphs

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.

Data-driven learning of robust nonlocal physics from high-fidelity synthetic data

no code implementations17 May 2020 Huaiqian You, Yue Yu, Nathaniel Trask, Mamikon Gulian, Marta D'Elia

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori.

Urban Anomaly Analytics: Description, Detection, and Prediction

no code implementations25 Apr 2020 Mingyang Zhang, Tong Li, Yue Yu, Yong Li, Pan Hui, Yu Zheng

Urban anomalies may result in loss of life or property if not handled properly.

SPAN: A Stochastic Projected Approximate Newton Method

no code implementations10 Feb 2020 Xunpeng Huang, Xianfeng Liang, Zhengyang Liu, Yitan Li, Linyun Yu, Yue Yu, Lei LI

SPAN computes the inverse of the Hessian matrix via low-rank approximation and stochastic Hessian-vector products.

Acutum: When Generalization Meets Adaptability

no code implementations25 Sep 2019 Xunpeng Huang, Zhengyang Liu, Zhe Wang, Yue Yu, Lei LI

To the best of our knowledge, Acutum is the first adaptive gradient method without second moments.

Modeling Long-Range Context for Concurrent Dialogue Acts Recognition

no code implementations2 Sep 2019 Yue Yu, Siyao Peng, Grace Hui Yang

Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues.

DAG-GNN: DAG Structure Learning with Graph Neural Networks

3 code implementations22 Apr 2019 Yue Yu, Jie Chen, Tian Gao, Mo Yu

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes.

Characterizing Malicious Edges targeting on Graph Neural Networks

no code implementations27 Sep 2018 Xiaojun Xu, Yue Yu, Bo Li, Le Song, Chengfeng Liu, Carl Gunter

Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets.

Graph Generation Link Prediction +1

Double Quantization for Communication-Efficient Distributed Optimization

no code implementations NeurIPS 2019 Yue Yu, Jiaxiang Wu, Longbo Huang

In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients.

Distributed Optimization Quantization

Aurora: Providing Trusted System Services for Enclaves On an Untrusted System

1 code implementation10 Feb 2018 Hongliang Liang, Mingyu Li, Qiong Zhang, Yue Yu, Lin Jiang, Yixiu Chen

Intel SGX provisions shielded executions for security-sensitive computation, but lacks support for trusted system services (TSS), such as clock, network and filesystem.

Cryptography and Security

Optimal Cooperative Inference

no code implementations24 May 2017 Scott Cheng-Hsin Yang, Yue Yu, Arash Givchi, Pei Wang, Wai Keen Vong, Patrick Shafto

Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.

Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization

no code implementations11 May 2017 Yue Yu, Longbo Huang

We consider the stochastic composition optimization problem proposed in \cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning.

A Mandarin-English Code-Switching Corpus

no code implementations LREC 2012 Ying Li, Yue Yu, Pascale Fung

Generally the existing monolingual corpora are not suitable for large vocabulary continuous speech recognition (LVCSR) of code-switching speech.

Boundary Detection Language Identification +2

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