Search Results for author: Yitong Li

Found 43 papers, 15 papers with code

UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues

no code implementations15 Oct 2021 Xinyan Zhao, Bin He, Yasheng Wang, Yitong Li, Fei Mi, Yajiao Liu, Xin Jiang, Qun Liu, Huanhuan Chen

With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems.

Task-Oriented Dialogue Systems

CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems

no code implementations10 Sep 2021 Fei Mi, Yitong Li, Yasheng Wang, Xin Jiang, Qun Liu

As labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge in practice is to learn different tasks with the least amount of labeled data.

Few-Shot Learning Intent Classification +1

Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training

no code implementations6 Sep 2021 Minghao Wu, Yitong Li, Meng Zhang, Liangyou Li, Gholamreza Haffari, Qun Liu

In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.

Machine Translation Translation

Rethinking Grid-Forming and Grid-Following Inverters: A Duality Theory

1 code implementation27 May 2021 Yitong Li, Yunjie Gu, Timothy C. Green

Power electronic converters for integrating renewable energy resources into power systems can be divided into grid-forming and grid-following inverters.

Mapping of Dynamics between Mechanical and Electrical Ports in SG-IBR Composite Grids

1 code implementation13 May 2021 Yitong Li, Yunjie Gu, Timothy C. Green

The SG-dominated grid is traditionally analyzed in a mechanical-centric view which ignores fast electrical dynamics and focuses on the torque-speed dynamics.

The nature of synchronization in power systems: a revelation from communication theory

no code implementations30 Mar 2021 Yunjie Gu, Yitong Li, Timothy C. Green

The large-scale integration of converter-interfaced resources in electrical power systems raises new stability threats which call for a new theoretic framework for modelling and analysis.

Automated Generation of Interorganizational Disaster Response Networks through Information Extraction

no code implementations27 Feb 2021 Yitong Li, Duoduo Liao, Jundong Li, Wenying Ji

When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders.

Disaster Response Named Entity Recognition +1

Participation Analysis in Impedance Models: The Grey-Box Approach for Power System Stability

1 code implementation8 Feb 2021 Yue Zhu, Yunjie Gu, Yitong Li, Timothy C. Green

This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system.

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

2 code implementations Findings of the Association for Computational Linguistics 2020 Lingjuan Lyu, Xuanli He, Yitong Li

It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy.

Fairness

CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair

1 code implementation18 Jul 2020 Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, Lin Tan

To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages.

Ensemble Learning Machine Translation +2

Towards Differentially Private Text Representations

no code implementations25 Jun 2020 Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model.

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

no code implementations ACL 2020 Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin

Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.

Conditional Text Generation Representation Learning +2

Automated Abstraction of Operation Processes from Unstructured Text for Simulation Modeling

no code implementations25 Apr 2020 Yitong Li, Wenying Ji, Simaan M. AbouRizk

Overall, this research enhances the state-of-the-art simulation modeling through achieving automated abstraction of operation processes, which largely reduces modelers' interpretation load and ensures the reliability of the abstracted operation processes.

Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions

no code implementations2 Mar 2020 Yitong Li, Dianqi Li, Sushant Prakash, Peng Wang

To improve the interpretability in the dual encoder models, we design a novel regularization loss to minimize the mutual information between unimportant words and desired labels, in addition to the original attention method, so that important words are emphasized while unimportant words are de-emphasized.

Word Embeddings

Estimating Uncertainty Intervals from Collaborating Networks

2 code implementations12 Feb 2020 Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

To estimate uncertainty in regression, one could modify a deep neural network to predict coverage intervals, such as by predicting the mean and standard deviation.

Decision Making

Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

1 code implementation NeurIPS 2019 Kevin J Liang, Guoyin Wang, Yitong Li, Ricardo Henao, Lawrence Carin

We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.

LMVP: Video Predictor with Leaked Motion Information

no code implementations24 Jun 2019 Dong Wang, Yitong Li, Wei Cao, Liqun Chen, Qi Wei, Lawrence Carin

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.

Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks

no code implementations14 Jun 2019 Yitong Li, Wenying Ji

This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information.

Decision Making

Towards Fair and Privacy-Preserving Federated Deep Models

1 code implementation4 Jun 2019 Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, Kee Siong Ng

This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.

Fairness Federated Learning +1

On Target Shift in Adversarial Domain Adaptation

no code implementations15 Mar 2019 Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, David E. Carlson

In this work, we propose a method called Domain Adversarial nets for Target Shift (DATS) to address label shift while learning a domain invariant representation.

Domain Adaptation

Sequential Attention GAN for Interactive Image Editing

no code implementations20 Dec 2018 Yu Cheng, Zhe Gan, Yitong Li, Jingjing Liu, Jianfeng Gao

The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the provided textual description; 2) step-by-step region-level modification to maintain visual consistency across the generated image sequence in each session.

Text-to-Image Generation

Extracting Relationships by Multi-Domain Matching

1 code implementation NeurIPS 2018 Yitong Li, Michael Murias, Geraldine Dawson, David E. Carlson

This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial.

Domain Adaptation Time Series +1

Diffusion Maps for Textual Network Embedding

no code implementations NeurIPS 2018 Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin

Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices.

General Classification Link Prediction +1

Non-Oscillatory Pattern Learning for Non-Stationary Signals

no code implementations21 May 2018 Jieren Xu, Yitong Li, David Dunson, Ingrid Daubechies, Haizhao Yang

This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for several oscillatory data analysis problems including signal decomposition, super-resolution, and signal sub-sampling.

Super-Resolution

Towards Robust and Privacy-preserving Text Representations

3 code implementations ACL 2018 Yitong Li, Timothy Baldwin, Trevor Cohn

Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes.

What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training

1 code implementation NAACL 2018 Yitong Li, Timothy Baldwin, Trevor Cohn

Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in training.

Domain Adaptation Language Identification +1

Multi-Label Learning from Medical Plain Text with Convolutional Residual Models

no code implementations15 Jan 2018 Xinyuan Zhang, Ricardo Henao, Zhe Gan, Yitong Li, Lawrence Carin

Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector.

General Classification Multi-Label Classification +2

Targeting EEG/LFP Synchrony with Neural Nets

no code implementations NeurIPS 2017 Yitong Li, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, David E. Carlson

We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e. g., conventional deep learning methods).

EEG

Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering

no code implementations29 Oct 2017 Wenying Ji, Simaan M. AbouRizk, Osmar R. Zaiane, Yitong Li

This paper proposes an uncertain data clustering approach to quantitatively analyze the complexity of prefabricated construction components through the integration of quality performance-based measures with associated engineering design information.

BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning

no code implementations WS 2017 Yitong Li, Trevor Cohn, Timothy Baldwin

This paper describes our submission to the sentiment analysis sub-task of {``}Build It, Break It: The Language Edition (BIBI){''}, on both the builder and breaker sides.

Q-Learning Sentiment Analysis +1

Robust Training under Linguistic Adversity

1 code implementation EACL 2017 Yitong Li, Trevor Cohn, Timothy Baldwin

Deep neural networks have achieved remarkable results across many language processing tasks, however they have been shown to be susceptible to overfitting and highly sensitive to noise, including adversarial attacks.

Sentiment Analysis Speech Recognition +1

Learning Robust Representations of Text

1 code implementation EMNLP 2016 Yitong Li, Trevor Cohn, Timothy Baldwin

Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks.

EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video

no code implementations8 Jun 2015 Guangnan Ye, Yitong Li, Hongliang Xu, Dong Liu, Shih-Fu Chang

Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207%.

Event Detection Hierarchical structure

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