Search Results for author: Yitong Li

Found 61 papers, 22 papers with code

Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables

no code implementations2 Dec 2022 Bin Sun, Yitong Li, Fei Mi, Weichao Wang, Yiwei Li, Kan Li

Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables.

Dialogue Generation Response Generation

Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder

no code implementations1 Dec 2022 Bin Sun, Shaoxiong Feng, Yiwei Li, Weichao Wang, Fei Mi, Yitong Li, Kan Li

Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems.

Dialogue Generation Semantic Segmentation

An Overview on Controllable Text Generation via Variational Auto-Encoders

1 code implementation15 Nov 2022 Haoqin Tu, Yitong Li

Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language.

Text Generation

CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN

1 code implementation15 Jul 2022 Matan Atad, Vitalii Dmytrenko, Yitong Li, Xinyue Zhang, Matthias Keicher, Jan Kirschke, Bene Wiestler, Ashkan Khakzar, Nassir Navab

Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.

Whole-System First-Swing Stability of Inverter-Based Inertia-Free Power Systems

1 code implementation7 Jul 2022 Yitong Li, Yunjie Gu

The emphasis on inertia for system stability has been a long-held tradition in conventional grids.

Impedance-based Root-cause Analysis: Comparative Study of Impedance Models and Calculation of Eigenvalue Sensitivity

1 code implementation4 Apr 2022 Yue Zhu, Yunjie Gu, Yitong Li, Timothy C. Green

Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available.

RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion

no code implementations ACL 2022 Kai Chen, Ye Wang, Yitong Li, Aiping Li

Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.

Knowledge Graph Completion Link Prediction +2

Compilable Neural Code Generation with Compiler Feedback

no code implementations Findings (ACL) 2022 Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu

Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering.

Code Completion Code Generation +3

Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks

1 code implementation16 Feb 2022 Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng

The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e. g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice.

Bias Detection Open-Domain Dialog

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation

no code implementations COLING 2022 Yihe Wang, Yitong Li, Yasheng Wang, Fei Mi, Pingyi Zhou, Xin Wang, Jin Liu, Xin Jiang, Qun Liu

Experiments over publicly available datasets demonstrate that our method can help models generate better responses, even such training data are usually impressed as low-quality data.

Dialogue Generation Retrieval

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.

dialog state tracking Few-Shot Learning +3

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

no code implementations EMNLP 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

Revisiting 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 Intrinsic Communication in Power Systems: A New Perspective to Understand Synchronization Stability

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

Based on this isomorphism, we revisit power system synchronization stability from a communication perspective and thereby establish a theory that unifies the synchronization dynamics of heterogeneous power apparatuses.

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 +2

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.


Polarized hyperspectral imaging with single fiber bundle via incoherent light transmission matrix approach

no code implementations11 Jan 2021 Yitong Li, Zhengbo Zhu, Ze Li, Donglin Ma

The scattering of multispectral incoherent light is a common and unfavorable signal scrambling in natural scenes.

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.


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 +3

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

1 code implementation12 Feb 2020 Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions.

Decision Making regression

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.

Benchmarking Fairness +3

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 Analysis +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

PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals

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

This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e. g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data.

regression Super-Resolution

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

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.

Privacy Preserving

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 +3

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).

Electroencephalogram (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 reinforcement-learning +3

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 Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.