Search Results for author: Hao Peng

Found 95 papers, 54 papers with code

Twist Decoding: Diverse Generators Guide Each Other

1 code implementation19 May 2022 Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith

Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models.

Machine Translation Text Generation

Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

no code implementations18 Mar 2022 Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning Representation Learning

Curvature Graph Generative Adversarial Networks

1 code implementation3 Mar 2022 JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng

In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.

Towards Unsupervised Deep Graph Structure Learning

1 code implementation17 Jan 2022 Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.

Contrastive Learning Graph structure learning

Sequential Recommendation via Stochastic Self-Attention

1 code implementation16 Jan 2022 Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu

We further argue that BPR loss has no constraint on positive and sampled negative items, which misleads the optimization.

Sequential Recommendation

Graph Structure Learning with Variational Information Bottleneck

1 code implementation16 Dec 2021 Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.

Graph structure learning

A Self-supervised Mixed-curvature Graph Neural Network

no code implementations10 Dec 2021 Li Sun, Zhongbao Zhang, Junda Ye, Hao Peng, Jiawei Zhang, Sen Su, Philip S. Yu

Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.

Contrastive Learning Graph Representation Learning

POLLA: Enhancing the Local Structure Awareness in Long Sequence Spatial-temporal Modeling

1 code implementation TIST 2021 2021 Haoyi Zhou, Hao Peng, Jieqi Peng, Shuai Zhang, JianXin Li

Extensive experiments are conducted on five large-scale datasets, which demonstrate that our method achieves state-of-the-art performance and validates the effectiveness brought by local structure information.

Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network

no code implementations28 Nov 2021 Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip S. Yu, Kannan Achan

In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.

Recommendation Systems

Federated Social Recommendation with Graph Neural Network

no code implementations21 Nov 2021 Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, Philip S. Yu

However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.

Federated Learning Recommendation Systems

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

no code implementations20 Nov 2021 Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.

Contrastive Learning Graph Representation Learning +1

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

3D Object Detection Combining Semantic and Geometric Features from Point Clouds

no code implementations10 Oct 2021 Hao Peng, Guofeng Tong, Zheng Li, Yaqi Wang, Yuyuan Shao

The SGNet proposed in this paper has achieved state-of-the-art results for 3D object detection in the KITTI dataset, especially in the detection of small-size objects such as cyclists.

3D Object Detection

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Event Extraction Graph Attention +1

Transferring Knowledge Distillation for Multilingual Social Event Detection

1 code implementation6 Aug 2021 Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang

Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.

Cross-Lingual Word Embeddings Event Detection +2

Multiplex Graph Networks for Multimodal Brain Network Analysis

1 code implementation31 Jul 2021 Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.

A Compact Survey on Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +1

Noised Consistency Training for Text Summarization

no code implementations28 May 2021 Junnan Liu, Qianren Mao, Bang Liu, Hao Peng, Hongdong Zhu, JianXin Li

In this paper, we argue that this limitation can be overcome by a semi-supervised approach: consistency training which is to leverage large amounts of unlabeled data to improve the performance of supervised learning over a small corpus.

Abstractive Text Summarization

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Differentially Private Federated Knowledge Graphs Embedding

1 code implementation17 May 2021 Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, JianXin Li

However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.

Knowledge Graph Embedding Knowledge Graphs +2

Federated Multi-View Learning for Private Medical Data Integration and Analysis

no code implementations4 May 2021 Sicong Che, Hao Peng, Lichao Sun, Yong Chen, Lifang He

This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).

Federated Learning MULTI-VIEW LEARNING

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Node Classification Node Clustering +1

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

1 code implementation16 Apr 2021 Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu

To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.

Fraud Detection Node Classification +1

HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

1 code implementation NAACL 2021 Zhongfen Deng, Hao Peng, Dongxiao He, JianXin Li, Philip S. Yu

The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.

General Classification Representation Learning +1

Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs

no code implementations6 Apr 2021 Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu

To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Event Detection

Finetuning Pretrained Transformers into RNNs

1 code implementation EMNLP 2021 Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith

Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.

Language Modelling Machine Translation +1

Random Feature Attention

no code implementations ICLR 2021 Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A. Smith, Lingpeng Kong

RFA can be used as a drop-in replacement for conventional softmax attention and offers a straightforward way of learning with recency bias through an optional gating mechanism.

Language Modelling Machine Translation +2

FedMood: Federated Learning on Mobile Health Data for Mood Detection

1 code implementation6 Feb 2021 Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, Lifang He

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.

Depression Detection Federated Learning +1

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +3

Heterogeneous Similarity Graph Neural Network on Electronic Health Records

no code implementations17 Jan 2021 Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu

EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.

Infusing Finetuning with Semantic Dependencies

1 code implementation10 Dec 2020 Zhaofeng Wu, Hao Peng, Noah A. Smith

For natural language processing systems, two kinds of evidence support the use of text representations from neural language models "pretrained" on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia).

Natural Language Understanding

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

1 code implementation COLING 2020 Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.

Decision Making

Learning from Context or Names? An Empirical Study on Neural Relation Extraction

1 code implementation EMNLP 2020 Hao Peng, Tianyu Gao, Xu Han, Yankai Lin, Peng Li, Zhiyuan Liu, Maosong Sun, Jie zhou

We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks.

Relation Extraction

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

no code implementations NeurIPS 2020 Hu Liu, Jing Lu, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan

First, conventional attentions mostly limit the attention field only to a single user's behaviors, which is not suitable in e-commerce where users often hunt for new demands that are irrelevant to any historical behaviors.

Click-Through Rate Prediction

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

1 code implementation26 Sep 2020 Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.

Graph Attention Text Generation

Contextualized Perturbation for Textual Adversarial Attack

1 code implementation NAACL 2021 Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, Bill Dolan

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.

Adversarial Attack Language Modelling

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

no code implementations12 Aug 2020 Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.

Adversarial Directed Graph Embedding

1 code implementation9 Aug 2020 Shijie Zhu, JianXin Li, Hao Peng, Senzhang Wang, Lifang He

To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.

Graph Embedding Graph Mining

A Mixture of h - 1 Heads is Better than h Heads

no code implementations ACL 2020 Hao Peng, Roy Schwartz, Dianqi Li, Noah A. Smith

Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks.

Language Modelling Machine Translation +1

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations23 Jun 2020 Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

online learning Representation Learning

Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation

2 code implementations ICLR 2021 Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah A. Smith

We show that the speed disadvantage for autoregressive baselines compared to non-autoregressive methods has been overestimated in three aspects: suboptimal layer allocation, insufficient speed measurement, and lack of knowledge distillation.

Knowledge Distillation Machine Translation +1

Category-Specific CNN for Visual-aware CTR Prediction at

no code implementations18 Jun 2020 Hu Liu, Jing Lu, Hao Yang, Xiwei Zhao, Sulong Xu, Hao Peng, Zehua Zhang, Wenjie Niu, Xiaokun Zhu, Yongjun Bao, Weipeng Yan

Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.

Click-Through Rate Prediction

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine

1 code implementation16 May 2020 Hao Peng, Pei Chen, Rui Liu

Making accurate multi-step-ahead prediction for a complex system is a challenge for many practical applications, especially when only short-term time-series data are available.

Time Series

A Mixture of $h-1$ Heads is Better than $h$ Heads

no code implementations13 May 2020 Hao Peng, Roy Schwartz, Dianqi Li, Noah A. Smith

Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks.

Language Modelling Machine Translation +1

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

1 code implementation1 May 2020 Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng

In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.

Fraud Detection

Face Beautification: Beyond Makeup Transfer

1 code implementation8 Dec 2019 Xudong Liu, Ruizhe Wang, Chih-Fan Chen, Minglei Yin, Hao Peng, Shukhan Ng, Xin Li

Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework of face beautification.


Digital Twin: Acquiring High-Fidelity 3D Avatar from a Single Image

no code implementations7 Dec 2019 Ruizhe Wang, Chih-Fan Chen, Hao Peng, Xudong Liu, Oliver Liu, Xin Li

We present an approach to generate high fidelity 3D face avatar with a high-resolution UV texture map from a single image.

Face Model

RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved

1 code implementation18 Nov 2019 JianXin Li, Cheng Ji, Hao Peng, Yu He, Yangqiu Song, Xinmiao Zhang, Fanzhang Peng

However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.

Network Embedding

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

Classification General Classification +3

Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu

In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model.

Event Detection

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren

Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.

Link Prediction Multi-Label Classification +1

Understanding Beauty via Deep Facial Features

no code implementations30 Jan 2019 Xudong Liu, Tao Li, Hao Peng, Iris Chuoying Ouyang, Taehwan Kim, Ruizhe Wang

The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability.

Graph Convolutional Neural Networks via Motif-based Attention

no code implementations11 Nov 2018 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, Philip S. Yu

Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information.

General Classification Graph Classification

Modeling relation paths for knowledge base completion via joint adversarial training

1 code implementation14 Oct 2018 Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, Philip S. Yu, Min He, Linfeng Du, Lihong Wang

By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.

Knowledge Base Completion

Rational Recurrences

1 code implementation EMNLP 2018 Hao Peng, Roy Schwartz, Sam Thomson, Noah A. Smith

We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs.

Language Modelling Text Classification

Backpropagating through Structured Argmax using a SPIGOT

1 code implementation ACL 2018 Hao Peng, Sam Thomson, Noah A. Smith

We introduce the structured projection of intermediate gradients optimization technique (SPIGOT), a new method for backpropagating through neural networks that include hard-decision structured predictions (e. g., parsing) in intermediate layers.

Dependency Parsing reinforcement-learning +2

Learning Joint Semantic Parsers from Disjoint Data

2 code implementations NAACL 2018 Hao Peng, Sam Thomson, Swabha Swayamdipta, Noah A. Smith

We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap.

Dependency Parsing Frame +1

"You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates

no code implementations23 Feb 2018 Chenhao Tan, Hao Peng, Noah A. Smith

We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situation.

Improving Orbit Prediction Accuracy through Supervised Machine Learning

no code implementations15 Jan 2018 Hao Peng, Xiaoli Bai

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already.

Semi-supervised Structured Prediction with Neural CRF Autoencoder

1 code implementation EMNLP 2017 Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser

In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems.

Part-Of-Speech Tagging POS +1

Deep Multitask Learning for Semantic Dependency Parsing

1 code implementation ACL 2017 Hao Peng, Sam Thomson, Noah A. Smith

We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms.

Dependency Parsing Semantic Dependency Parsing

A Convolutional Attention Network for Extreme Summarization of Source Code

5 code implementations9 Feb 2016 Miltiadis Allamanis, Hao Peng, Charles Sutton

Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension.

Extreme Summarization Translation

A Comparative Study on Regularization Strategies for Embedding-based Neural Networks

no code implementations EMNLP 2015 Hao Peng, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu, Zhi Jin

This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP.

Building Program Vector Representations for Deep Learning

1 code implementation11 Sep 2014 Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang

In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis.

Representation Learning

EigenGP: Gaussian Process Models with Adaptive Eigenfunctions

1 code implementation2 Jan 2014 Hao Peng, Yuan Qi

In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements--eigenfunctions of a GP prior--and prior precisions in a sparse finite model.

Gaussian Processes

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