Search Results for author: Tong Wang

Found 63 papers, 19 papers with code

Large Batch Optimization for Object Detection: Training COCO in 12 Minutes

no code implementations ECCV 2020 Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Yao-Wei Wang, Jinqiao Wang, Ming Tang

Most of existing object detectors usually adopt a small training batch size ( ~16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure.

object-detection Object Detection

Transparency Promotion with Model-Agnostic Linear Competitors

no code implementations ICML 2020 Hassan Rafique, Tong Wang, Qihang Lin, Arshia Singhani

We propose a novel type of hybrid model for multi-class classification, which utilizes competing linear models to collaborate with an existing black-box model, promoting transparency in the decision-making process.

Decision Making Multi-class Classification

Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations

no code implementations ACL (ECNLP) 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.

Entity Resolution

Dual Contrastive Attributed Graph Clustering Network

1 code implementation16 Jun 2022 Tong Wang, Guanyu Yang, Junhua Wu, Qijia He, Zhenquan Zhang

Attributed graph clustering is one of the most important tasks in graph analysis field, the goal of which is to group nodes with similar representations into the same cluster without manual guidance.

Contrastive Learning Data Augmentation +2

Multi-View Substructure Learning for Drug-Drug Interaction Prediction

no code implementations28 Mar 2022 Zimeng Li, Shichao Zhu, Bin Shao, Tie-Yan Liu, Xiangxiang Zeng, Tong Wang

Drug-drug interaction (DDI) prediction provides a drug combination strategy for systemically effective treatment.

Better Language Model with Hypernym Class Prediction

1 code implementation ACL 2022 He Bai, Tong Wang, Alessandro Sordoni, Peng Shi

Class-based language models (LMs) have been long devised to address context sparsity in $n$-gram LMs.

Language Modelling

Functional universality in slow-growing microbial communities arises from thermodynamic constraints

no code implementations11 Mar 2022 Ashish B. George, Tong Wang, Sergei Maslov

To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network.

Direct Molecular Conformation Generation

1 code implementation3 Feb 2022 Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, Tie-Yan Liu

In this work, we propose a method that directly predicts the coordinates of atoms.

DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points

no code implementations13 Dec 2021 Zhengfei Kuang, Jiaman Li, Mingming He, Tong Wang, Yajie Zhao

To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points.

Backdoor Attack through Frequency Domain

1 code implementation22 Nov 2021 Tong Wang, Yuan YAO, Feng Xu, Shengwei An, Hanghang Tong, Ting Wang

We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.

Autonomous Driving Backdoor Attack

Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP

no code implementations EMNLP 2021 Trapit Bansal, Karthick Gunasekaran, Tong Wang, Tsendsuren Munkhdalai, Andrew McCallum

Meta-learning considers the problem of learning an efficient learning process that can leverage its past experience to accurately solve new tasks.

Few-Shot Learning

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

no code implementations14 Oct 2021 Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.

Drug Discovery Pose Prediction

Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis

1 code implementation9 Oct 2021 Mu Yang, Shaojin Ding, Tianlong Chen, Tong Wang, Zhangyang Wang

This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system, where each language was seen as an individual task and was learned sequentially and continually.

Speech Synthesis Text-To-Speech Synthesis

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations

no code implementations4 Jun 2021 Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.

Interpretable Machine Learning

Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems

no code implementations NAACL 2021 Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu

In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.

Entity Resolution Intent Classification +1

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine Learning

no code implementations6 May 2021 Tong Wang, Jingyi Yang, Yunyi Li, Boxiang Wang

We propose Partially Interpretable Estimators (PIE) which attribute a prediction to individual features via an interpretable model, while a (possibly) small part of the PIE prediction is attributed to the interaction of features via a black-box model, with the goal to boost the predictive performance while maintaining interpretability.

Interpretable Machine Learning

Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations

no code implementations6 Apr 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.

Entity Resolution

Adaptive Class Suppression Loss for Long-Tail Object Detection

1 code implementation CVPR 2021 Tong Wang, Yousong Zhu, Chaoyang Zhao, Wei Zeng, Jinqiao Wang, Ming Tang

To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies.

object-detection Object Detection

Quantum Transport in Two-Dimensional WS$_2$ with High-Efficiency Carrier Injection Through Indium Alloy Contacts

no code implementations4 Feb 2021 Chit Siong Lau, Jing Yee Chee, Yee Sin Ang, Shi Wun Tong, Liemao Cao, Zi-En Ooi, Tong Wang, Lay Kee Ang, Yan Wang, Manish Chhowalla, Kuan Eng Johnson Goh

Here, temperature-dependent transfer length measurements are performed on chemical vapour deposition grown single-layer and bilayer WS$_2$ devices with indium alloy contacts.

Materials Science Mesoscale and Nanoscale Physics

A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis

no code implementations12 Jan 2021 Shaosheng Xu, Jinde Cao, Yichao Cao, Tong Wang

As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure.

Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness

no code implementations17 Nov 2020 Tong Wang, Maytal Saar-Tsechansky

We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias.

Active Learning Fairness

An Empirical Study on Neural Keyphrase Generation

no code implementations NAACL 2021 Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, Daqing He

Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them.

Keyphrase Generation

Same-Day Delivery with Fairness

no code implementations19 Jul 2020 Xinwei Chen, Tong Wang, Barrett W. Thomas, Marlin W. Ulmer

The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic.

Fairness Q-Learning

Interpretable Sequence Classification Via Prototype Trajectory

1 code implementation3 Jul 2020 Dat Hong, Stephen S. Baek, Tong Wang

We propose a novel interpretable deep neural network for text classification, called ProtoryNet, based on a new concept of prototype trajectories.

Classification General Classification +1

Exploring and Predicting Transferability across NLP Tasks

1 code implementation EMNLP 2020 Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer

We also develop task embeddings that can be used to predict the most transferable source tasks for a given target task, and we validate their effectiveness in experiments controlled for source and target data size.

Language Modelling Part-Of-Speech Tagging +3

Task-adaptive Asymmetric Deep Cross-modal Hashing

no code implementations1 Apr 2020 Fengling Li, Tong Wang, Lei Zhu, Zheng Zhang, Xinhua Wang

Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.

Cross-Modal Retrieval

Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication

no code implementations23 Feb 2020 Azhar Hussain, Tong Wang, Cao Jiahua

We consider a system to optimize duration of traffic signals using multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X) communication.

reinforcement-learning

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

no code implementations19 Feb 2020 Chen Liao, Tong Wang, Sergei Maslov, Joao B. Xavier

We used the three models to study each community's limits of robustness to perturbations such as variations in resource supply, antibiotic treatments and invasion by other "cheaters" species.

Interpretable Companions for Black-Box Models

no code implementations10 Feb 2020 Danqing Pan, Tong Wang, Satoshi Hara

We present an interpretable companion model for any pre-trained black-box classifiers.

Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs

no code implementations9 Nov 2019 Tong Wang, Fujie Jin, Yu, Hu, Yuan Cheng

The prediction model and the interpretable insights can be applied to assist fundraisers with better promoting their fundraising campaigns and can potentially help crowdfunding platforms to provide more timely feedback to all fundraisers.

Time Series Time Series Clustering

Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models

no code implementations23 Sep 2019 Hassan Rafique, Tong Wang, Qihang Lin

Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications.

Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence

1 code implementation9 Sep 2019 Rui Meng, Xingdi Yuan, Tong Wang, Peter Brusilovsky, Adam Trischler, Daqing He

Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation.

Keyphrase Generation

Metalearned Neural Memory

1 code implementation NeurIPS 2019 Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler

We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning.

Question Answering reinforcement-learning

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model

no code implementations10 May 2019 Tong Wang, Qihang Lin

The interpretable model substitutes the black-box model on a subset of data where the black-box is overkill or nearly overkill, gaining transparency at no or low cost of the predictive accuracy.

Interpretable Machine Learning

Next Hit Predictor - Self-exciting Risk Modeling for Predicting Next Locations of Serial Crimes

no code implementations13 Dec 2018 Yunyi Li, Tong Wang

Our goal is to predict the location of the next crime in a crime series, based on the identified previous offenses in the series.

Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations

1 code implementation NeurIPS 2018 Tong Wang

We present the Multi-value Rule Set (MRS) for interpretable classification with feature efficient presentations.

General Classification

An Interpretable Model with Globally Consistent Explanations for Credit Risk

no code implementations30 Nov 2018 Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.

Interpretable Patient Mortality Prediction with Multi-value Rule Sets

1 code implementation6 Jul 2018 Tong Wang, Veerajalandhar Allareddy, Sankeerth Rampa, Veerasathpurush Allareddy

We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency.

Mortality Prediction

Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute

1 code implementation12 Feb 2018 Tong Wang

This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case.

Decision Making Interpretable Machine Learning

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect

no code implementations16 Oct 2017 Tong Wang, Cynthia Rudin

The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}.

Causal Inference

Multi-Value Rule Sets

no code implementations15 Oct 2017 Tong Wang

MARS introduces a more generalized form of association rules that allows multiple values in a condition.

Annotating High-Level Structures of Short Stories and Personal Anecdotes

no code implementations LREC 2018 Boyang Li, Beth Cardier, Tong Wang, Florian Metze

Stories are a vital form of communication in human culture; they are employed daily to persuade, to elicit sympathy, or to convey a message.

Predicting the Quality of Short Narratives from Social Media

no code implementations8 Jul 2017 Tong Wang, Ping Chen, Boyang Li

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality.

Active Learning

A Joint Model for Question Answering and Question Generation

no code implementations5 Jun 2017 Tong Wang, Xingdi Yuan, Adam Trischler

We propose a generative machine comprehension model that learns jointly to ask and answer questions based on documents.

Question Answering Question Generation +1

A Semantic QA-Based Approach for Text Summarization Evaluation

no code implementations21 Apr 2017 Ping Chen, Fei Wu, Tong Wang, Wei Ding

In this paper, we will present some preliminary results on one especially useful and challenging problem in NLP system evaluation: how to pinpoint content differences of two text passages (especially for large pas-sages such as articles and books).

Machine Translation Natural Language Processing +3

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

11 code implementations28 Nov 2016 Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.

Machine Reading Comprehension Question Answering

Topic Modeling over Short Texts by Incorporating Word Embeddings

1 code implementation27 Sep 2016 Jipeng Qiang, Ping Chen, Tong Wang, Xindong Wu

Inferring topics from the overwhelming amount of short texts becomes a critical but challenging task for many content analysis tasks, such as content charactering, user interest profiling, and emerging topic detecting.

Word Embeddings

An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification

no code implementations13 Sep 2016 Tong Wang, Ping Chen, Kevin Amaral, Jipeng Qiang

Text simplification (TS) aims to reduce the lexical and structural complexity of a text, while still retaining the semantic meaning.

Text Simplification

Learning Optimized Or's of And's

no code implementations6 Nov 2015 Tong Wang, Cynthia Rudin

Or's of And's (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form.

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems

no code implementations28 Apr 2015 Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

In both cases, there are prior parameters that the user can set to encourage the model to have a desired size and shape, to conform with a domain-specific definition of interpretability.

General Classification Recommendation Systems

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