Search Results for author: Wei Wei

Found 120 papers, 27 papers with code

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation

1 code implementation12 Nov 2021 Yu Zhang, Wei Wei, Binxuan Huang, Kathleen M. Carley, Yan Zhang

Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.

Event Detection

Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework

no code implementations28 Oct 2021 Lifan Yuan, Yichi Zhang, Yangyi Chen, Wei Wei

Despite great success on many machine learning tasks, deep neural networks are still vulnerable to adversarial samples.

Adversarial Attack Language Modelling

Geometry-Entangled Visual Semantic Transformer for Image Captioning

no code implementations29 Sep 2021 Ling Cheng, Wei Wei, Feida Zhu, Yong liu, Chunyan Miao

However, those fusion-based models, they are still criticized for the lack of geometry information for inter and intra attention refinement.

Image Captioning

Approaching the Transient Stability Boundary of a Power System: Theory and Applications

no code implementations26 Sep 2021 Peng Yang, Feng Liu, Wei Wei, Zhaojian Wang

Estimating the stability boundary is a fundamental and challenging problem in transient stability studies.

Context-aware Entity Typing in Knowledge Graphs

1 code implementation Findings (EMNLP) 2021 Weiran Pan, Wei Wei, Xian-Ling Mao

Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue.

Entity Typing Knowledge Graphs

Storage and Transmission Capacity Requirements of a Remote Solar Power Generation System

no code implementations13 Sep 2021 Yue Chen, Wei Wei, Cheng Wang, Miadreza Shafie-khah, João P. S. Catalão

Large solar power stations usually locate in remote areas and connect to the main grid via a long transmission line.

Multi-granularity Textual Adversarial Attack with Behavior Cloning

1 code implementation EMNLP 2021 Yangyi Chen, Jin Su, Wei Wei

Furthermore, we propose a reinforcement-learning based method to train a multi-granularity attack agent through behavior cloning with the expert knowledge from our MAYA algorithm to further reduce the query times.

Adversarial Attack

Heterogeneous Graph Neural Network with Multi-view Representation Learning

no code implementations31 Aug 2021 Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu

Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph.

Graph Embedding Link Prediction +2

A Graph Data Augmentation Strategy with Entropy Preserving

no code implementations13 Jul 2021 Xue Liu, Dan Sun, Wei Wei

Under considerations of preserving graph entropy, we propose an effective strategy to generate perturbed training data using a stochastic mechanism but guaranteeing graph topology integrity and with only a small amount of graph entropy decaying.

Data Augmentation Node Classification

Locality Relationship Constrained Multi-view Clustering Framework

no code implementations11 Jul 2021 Xiangzhu Meng, Wei Wei, Wenzhe Liu

LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views.

MULTI-VIEW LEARNING

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

1 code implementation9 Jun 2021 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu

In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph.

Representation Learning Session-Based Recommendations

Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction

no code implementations6 Jun 2021 Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.

Exploiting Global Contextual Information for Document-level Named Entity Recognition

no code implementations2 Jun 2021 Zanbo Wang, Wei Wei, Xianling Mao, Shanshan Feng, Pan Zhou, Zhiyong He, Sheng Jiang

To this end, we propose a model called Global Context enhanced Document-level NER (GCDoc) to leverage global contextual information from two levels, i. e., both word and sentence.

Document-level Named Entity Recognition +1

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

no code implementations14 May 2021 Jun Shi, Huite Yi, Shulan Ruan, Zhaohui Wang, Xiaoyu Hao, Hong An, Wei Wei

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy.

Computed Tomography (CT)

On the Time-Inconsistent Deterministic Linear-Quadratic Control

no code implementations8 May 2021 Hongyan Cai, Danhong Chen, Yunfei Peng, Wei Wei

By studying the solvability of the Riccati equation, we show the existence and uniqueness of the linear equilibrium for the time-inconsistent LQ problem.

A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting

no code implementations6 Apr 2021 Kun Qian, Wei Wei, Zhou Yu

The most recent researches on domain adaption focus on giving the model a better initialization, rather than optimizing the adaptation process.

Domain Adaptation Meta-Learning

Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training

no code implementations31 Mar 2021 Jingcheng Zhou, Wei Wei, Zhiming Zheng

First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information.

Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas

no code implementations24 Mar 2021 Wei Wei, Li Guan, Yue Liu, Hao Kang, Haoxiang Li, Ying Wu, Gang Hua

By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.

HDR Reconstruction

Context-aware Biaffine Localizing Network for Temporal Sentence Grounding

1 code implementation CVPR 2021 Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Yu Cheng, Wei Wei, Zichuan Xu, Yulai Xie

This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query.

Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions

1 code implementation20 Mar 2021 Shuyu Lu, Ruoyu Chen, Wei Wei, Xinghua Lu

We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations.

A Data-Centric Framework for Composable NLP Workflows

1 code implementation EMNLP 2020 Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Haoying Zhang, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu

Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.

Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach

1 code implementation EMNLP 2021 Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei Wei

An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.

Model-based Reinforcement Learning Text Generation

Graph Classification Based on Skeleton and Component Features

no code implementations2 Feb 2021 Xue Liu, Wei Wei, Xiangnan Feng, Xiaobo Cao, Dan Sun

Most existing popular methods for learning graph embedding only consider fixed-order global structural features and lack structures hierarchical representation.

Classification General Classification +2

Large Deviations for SDE driven by Heavy-tailed Lévy Processes

no code implementations11 Jan 2021 Wei Wei, Qiao Huang, Jinqiao Duan

We obtain sample-path large deviations for a class of one-dimensional stochastic differential equations with bounded drifts and heavy-tailed L\'evy processes.

Probability 60H10, 60F10, 60J76

A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

1 code implementation2 Jan 2021 Houjin Yu, Xian-Ling Mao, Zewen Chi, Wei Wei, Heyan Huang

Recently, it has attracted much attention to build reliable named entity recognition (NER) systems using limited annotated data.

Ranked #2 on Named Entity Recognition on SciERC (using extra training data)

Low Resource Named Entity Recognition Named Entity Recognition +1

Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network

no code implementations2 Jan 2021 Xing Li, Wei Wei, Xiangnan Feng, Zhiming Zheng

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning.

Graph Representation Learning Link Prediction +1

FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition

no code implementations ICCV 2021 Lei Fan, Peixi Xiong, Wei Wei, Ying Wu

To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples.

Knowledge Distillation Scene Recognition

Generative Fairness Teaching

no code implementations1 Jan 2021 Rongmei Lin, Hanjun Dai, Li Xiong, Wei Wei

We propose a generative fairness teaching framework that provides a model with not only real samples but also synthesized samples to compensate the data biases during training.

Fairness

Context-Aware Temperature for Language Modeling

no code implementations1 Jan 2021 Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Da-Cheng Juan, Jia-Yu Pan, Wei Wei

Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule.

Language Modelling

Contextual Temperature for Language Modeling

no code implementations25 Dec 2020 Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Chang Juan

Temperature scaling has been widely used as an effective approach to control the smoothness of a distribution, which helps the model performance in various tasks.

Language Modelling

Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems

no code implementations21 Dec 2020 Tian Lan, Xian-Ling Mao, Zhipeng Zhao, Wei Wei, Heyan Huang

Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently.

Open-Domain Dialog

Ultra-Fast, Low-Storage, Highly Effective Coarse-grained Selection in Retrieval-based Chatbot by Using Deep Semantic Hashing

1 code implementation17 Dec 2020 Tian Lan, Xian-Ling Mao, Xiaoyan Gao, Wei Wei, Heyan Huang

Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it.

Chatbot

Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

no code implementations15 Dec 2020 E. A. Huerta, Asad Khan, Xiaobo Huang, Minyang Tian, Maksim Levental, Ryan Chard, Wei Wei, Maeve Heflin, Daniel S. Katz, Volodymyr Kindratenko, Dawei Mu, Ben Blaiszik, Ian Foster

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.

Distributed Computing Gravitational Wave Detection

Exploiting Group-level Behavior Pattern forSession-based Recommendation

no code implementations10 Dec 2020 Ziyang Wang, Wei Wei, Xian-Ling Mao, Xiao-Li Li, Shanshan Feng

In RNMSR, we propose to learn the user preference from both instance-level and group-level, respectively: (i) instance-level, which employs GNNs on a similarity-based item-pairwise session graph to capture the users' preference in instance-level.

Representation Learning Session-Based Recommendations

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

no code implementations10 Dec 2020 Daizong Liu, Shuangjie Xu, Xiao-Yang Liu, Zichuan Xu, Wei Wei, Pan Zhou

To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph.

Fine-tuning One-shot visual object segmentation +3

Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing

no code implementations9 Dec 2020 Honghao Gao, Xuejie Wang, Xiaojin Ma, Wei Wei, Shahid Mumtaz

First, we discuss the task dependency model, task priority model, energy consumption model, and average latency from the perspective of server clusters and multidependence on mobile tasks.

Decision Making Edge-computing +1 Distributed, Parallel, and Cluster Computing Multiagent Systems

Meta-Generating Deep Attentive Metric for Few-shot Classification

no code implementations3 Dec 2020 Lei Zhang, Fei Zhou, Wei Wei, Yanning Zhang

To mitigate this problem, we present a novel deep metric meta-generation method that turns to an orthogonal direction, ie, learning to adaptively generate a specific metric for a new FSL task based on the task description (eg, a few labelled samples).

Classification Few-Shot Learning +1

Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution

no code implementations3 Dec 2020 Jiangtao Nie, Lei Zhang, Wei Wei, Zhiqiang Lang, Yanning Zhang

One of the main reason comes from the fact that the predefined degeneration models (e. g. blur in spatial domain) utilized by most HSI SR methods often exist great discrepancy with the real one, which results in these deep models overfit and ultimately degrade their performance on real data.

Meta-Learning Super-Resolution

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization

no code implementations NeurIPS 2020 Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

To preserve the knowledge we learn from previous instances, we proposed a method to protect the path by restricting the gradient updates of one instance from overriding past updates calculated from previous instances if these instances are not similar.

Continual Learning Fine-tuning

Differentiable Top-k with Optimal Transport

no code implementations NeurIPS 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.

Information Retrieval

AirConcierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval

1 code implementation Findings of the Association for Computational Linguistics 2020 Chieh-Yang Chen, Pei-Hsin Wang, Shih-Chieh Chang, Da-Cheng Juan, Wei Wei, Jia-Yu Pan

Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms.

Task-Oriented Dialogue Systems Text-To-Sql

Exploring Global Information for Session-based Recommendation

no code implementations20 Nov 2020 Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu, Shanshan Feng

Based on BGNN, we propose a novel approach, called Session-based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring global item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of session level item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level item-transition information as a constraint to ensure the learned item embeddings are consistent with the global item-transition.

Session-Based Recommendations

User-based Network Embedding for Collective Opinion Spammer Detection

no code implementations16 Nov 2020 Ziyang Wang, Wei Wei, Xian-Ling Mao, Guibing Guo, Pan Zhou, Shanshan Feng

Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation.

Network Embedding

Target Guided Emotion Aware Chat Machine

no code implementations15 Nov 2020 Wei Wei, Jiayi Liu, Xianling Mao, Guibin Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.

A Survey on Recent Advances in Sequence Labeling from Deep Learning Models

no code implementations13 Nov 2020 Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e. g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc.

Chunking Information Retrieval +6

Deep Cross-modal Hashing via Margin-dynamic-softmax Loss

no code implementations6 Nov 2020 Rong-Cheng Tu, Xian-Ling Mao, Rongxin Tu, Binbin Bian, Wei Wei, Heyan Huang

Finally, by minimizing the novel \textit{margin-dynamic-softmax loss}, the modality-specific hashing networks can be trained to generate hash codes which can simultaneously preserve the cross-modal similarity and abundant semantic information well.

Cross-Modal Retrieval

Question Answering with Long Multiple-Span Answers

1 code implementation Findings of the Association for Computational Linguistics 2020 Ming Zhu, Aman Ahuja, Da-Cheng Juan, Wei Wei, Chandan K. Reddy

To this end, we present MASH-QA, a Multiple Answer Spans Healthcare Question Answering dataset from the consumer health domain, where answers may need to be excerpted from multiple, non-consecutive parts of text spanned across a long document.

Question Answering

Deep Kernel Supervised Hashing for Node Classification in Structural Networks

no code implementations26 Oct 2020 Jia-Nan Guo, Xian-Ling Mao, Shu-Yang Lin, Wei Wei, Heyan Huang

However, nearly all the existing network embedding based methods are hard to capture the actual category features of a node because of the linearly inseparable problem in low-dimensional space; meanwhile they cannot incorporate simultaneously network structure information and node label information into network embedding.

Classification General Classification +2

Differentiable Top-$k$ with Optimal Transport

no code implementations NeurIPS Workshop LMCA 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval

Which Kind Is Better in Open-domain Multi-turn Dialog,Hierarchical or Non-hierarchical Models? An Empirical Study

no code implementations7 Aug 2020 Tian Lan, Xian-Ling Mao, Wei Wei, He-Yan Huang

Thus, in this paper, we will measure systematically nearly all representative hierarchical and non-hierarchical models over the same experimental settings to check which kind is better.

Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs

no code implementations31 Jul 2020 Xing Li, Wei Wei, Xiangnan Feng, Xue Liu, Zhiming Zheng

The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.

Link Prediction Node Classification +1

Remix: Rebalanced Mixup

no code implementations8 Jul 2020 Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan

In this work, we propose a new regularization technique, Remix, that relaxes Mixup's formulation and enables the mixing factors of features and labels to be disentangled.

Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization

no code implementations7 Jul 2020 Li-Huang Tsai, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Cheng Juan

In this paper, we propose a noise-agnostic method to achieve robust neural network performance against any noise setting.

Object Detection Semantic Segmentation

A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms

no code implementations15 Jun 2020 Wei Wei, Bei Zhou, Georgios Leontidis

This paper proposes an enhanced natural language generation system combining the merits of both rule-based approaches and modern deep learning algorithms, boosting its performance to the extent where the generated textual content is capable of exhibiting agile human-writing styles and the content logic of which is highly controllable.

Text Generation

Ball k-means

no code implementations2 May 2020 Shuyin Xia, Daowan Peng, Deyu Meng, Changqing Zhang, Guoyin Wang, Zizhong Chen, Wei Wei

The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.

CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network

no code implementations28 Apr 2020 Shilin Qu, Fajie Yuan, Guibing Guo, Liguang Zhang, Wei Wei

Specifically, our framework divides proximal information units into chunks, and performs memory access at certain time steps, whereby the number of memory operations can be greatly reduced.

Chunking Recommendation Systems

PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems

1 code implementation6 Apr 2020 Tian Lan, Xian-Ling Mao, Wei Wei, Xiaoyan Gao, He-Yan Huang

Through extensive experiments, the learning-based metrics are demonstrated that they are the most effective evaluation metrics for open-domain generative dialogue systems.

Crowd Counting via Hierarchical Scale Recalibration Network

no code implementations7 Mar 2020 Zhikang Zou, Yifan Liu, Shuangjie Xu, Wei Wei, Shiping Wen, Pan Zhou

Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.

Crowd Counting

Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels

no code implementations26 Feb 2020 Daizong Liu, Shuangjie Xu, Pan Zhou, Kun He, Wei Wei, Zichuan Xu

In this work, we propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases by using a dynamic learnable adjacency matrix in graph structure to improve the diagnosis accuracy.

Curriculum Learning Multi-Label Classification

Differentiable Top-k Operator with Optimal Transport

no code implementations16 Feb 2020 Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.

Information Retrieval

When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation

no code implementations20 Dec 2019 Tian Lan, Xian-Ling Mao, He-Yan Huang, Wei Wei

Intuitively, a dialogue model that can control the timing of talking autonomously based on the conversation context can chat with humans more naturally.

Dialogue Generation

Overcoming Catastrophic Forgetting by Generative Regularization

no code implementations3 Dec 2019 Patrick H. Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai

In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework.

Bayesian Inference Continual Learning

Abstract Reasoning with Distracting Features

1 code implementation NeurIPS 2019 Kecheng Zheng, Zheng-Jun Zha, Wei Wei

Abstraction reasoning is a long-standing challenge in artificial intelligence.

Learning with Hierarchical Complement Objective

no code implementations17 Nov 2019 Hao-Yun Chen, Li-Huang Tsai, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation.

General Classification Image Classification +1

Table-to-Text Natural Language Generation with Unseen Schemas

no code implementations9 Nov 2019 Tianyu Liu, Wei Wei, William Yang Wang

In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training.

Text Generation

Natural Adversarial Sentence Generation with Gradient-based Perturbation

1 code implementation6 Sep 2019 Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh

This work proposes a novel algorithm to generate natural language adversarial input for text classification models, in order to investigate the robustness of these models.

Sentence Embeddings Sentiment Analysis +1

Stack-VS: Stacked Visual-Semantic Attention for Image Caption Generation

no code implementations5 Sep 2019 Wei Wei, Ling Cheng, Xian-Ling Mao, Guangyou Zhou, Feida Zhu

Recently, automatic image caption generation has been an important focus of the work on multimodal translation task.

XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs

no code implementations19 Aug 2019 Cheng Li, Abdul Dakkak, JinJun Xiong, Wei Wei, Lingjie Xu, Wen-mei Hwu

Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack).

Enhanced 3D convolutional networks for crowd counting

no code implementations12 Aug 2019 Zhikang Zou, Huiliang Shao, Xiaoye Qu, Wei Wei, Pan Zhou

Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting.

Crowd Counting

Deep Hashing for Signed Social Network Embedding

no code implementations12 Aug 2019 Jia-Nan Guo, Xian-Ling Mao, Xiao-Jian Jiang, Ying-Xiang Sun, Wei Wei, He-Yan Huang

Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification.

Link Prediction Network Embedding +1

Deep Cross-Modal Hashing with Hashing Functions and Unified Hash Codes Jointly Learning

no code implementations29 Jul 2019 Rong-Cheng Tu, Xian-Ling Mao, Bing Ma, Yong Hu, Tan Yan, Wei Wei, He-Yan Huang

Specifically, by an iterative optimization algorithm, DCHUC jointly learns unified hash codes for image-text pairs in a database and a pair of hash functions for unseen query image-text pairs.

Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning

no code implementations18 Jul 2019 Ying Shi, Wei Wei, Zhiming Zheng

Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes.

Generalized Zero-Shot Learning Object Recognition

Reinforcement Learning Driven Heuristic Optimization

no code implementations16 Jun 2019 Qingpeng Cai, Will Hang, Azalia Mirhoseini, George Tucker, Jingtao Wang, Wei Wei

In this paper, we introduce a novel framework to generate better initial solutions for heuristic algorithms using reinforcement learning (RL), named RLHO.

Combinatorial Optimization

Evaluating and Enhancing the Robustness of Dialogue Systems: A Case Study on a Negotiation Agent

no code implementations NAACL 2019 Minhao Cheng, Wei Wei, Cho-Jui Hsieh

Moreover, we show that with the adversarial training, we are able to improve the robustness of negotiation agents by 1. 5 points on average against all our attacks.

COCO-GAN: Conditional Coordinate Generative Adversarial Network

no code implementations ICLR 2019 Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch.

Image Generation Scene Generation

Vehicle Re-identification in Aerial Imagery: Dataset and Approach

no code implementations ICCV 2019 Peng Wang, Bingliang Jiao, Lu Yang, Yifei Yang, Shizhou Zhang, Wei Wei, Yanning Zhang

It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.

Vehicle Re-Identification

COCO-GAN: Generation by Parts via Conditional Coordinating

1 code implementation ICCV 2019 Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.

Face Generation

Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution

no code implementations24 Mar 2019 Lei Zhang, Zhiqiang Lang, Peng Wang, Wei Wei, Shengcai Liao, Ling Shao, Yanning Zhang

To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks.

Super-Resolution

Improving Adversarial Robustness via Guided Complement Entropy

2 code implementations ICCV 2019 Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model.

Adversarial Defense Adversarial Robustness +1

Complement Objective Training

1 code implementation ICLR 2019 Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes.

Language understanding Natural Language Understanding

Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era

no code implementations1 Feb 2019 Gabrielle Allen, Igor Andreoni, Etienne Bachelet, G. Bruce Berriman, Federica B. Bianco, Rahul Biswas, Matias Carrasco Kind, Kyle Chard, Minsik Cho, Philip S. Cowperthwaite, Zachariah B. Etienne, Daniel George, Tom Gibbs, Matthew Graham, William Gropp, Anushri Gupta, Roland Haas, E. A. Huerta, Elise Jennings, Daniel S. Katz, Asad Khan, Volodymyr Kindratenko, William T. C. Kramer, Xin Liu, Ashish Mahabal, Kenton McHenry, J. M. Miller, M. S. Neubauer, Steve Oberlin, Alexander R. Olivas Jr, Shawn Rosofsky, Milton Ruiz, Aaron Saxton, Bernard Schutz, Alex Schwing, Ed Seidel, Stuart L. Shapiro, Hongyu Shen, Yue Shen, Brigitta M. Sipőcz, Lunan Sun, John Towns, Antonios Tsokaros, Wei Wei, Jack Wells, Timothy J. Williams, JinJun Xiong, Zhizhen Zhao

We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.

Meta Architecture Search

1 code implementation NeurIPS 2019 Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.

Bayesian Inference Few-Shot Learning +1

AI Matrix - Synthetic Benchmarks for DNN

no code implementations27 Nov 2018 Wei Wei, Xu Lingjie, Jin Lingling, Zhang Wei, Zhang Tianjun

In this work, a synthetic benchmarks framework is firstly proposed to address the above drawbacks of AI benchmarks.

InstaNAS: Instance-aware Neural Architecture Search

2 code implementations26 Nov 2018 An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun

Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy.

Neural Architecture Search

Policy Certificates: Towards Accountable Reinforcement Learning

no code implementations7 Nov 2018 Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill

The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration.

AirDialogue: An Environment for Goal-Oriented Dialogue Research

1 code implementation EMNLP 2018 Wei Wei, Quoc Le, Andrew Dai, Jia Li

However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated.

Dialogue Generation

Towards Effective Deep Embedding for Zero-Shot Learning

no code implementations30 Aug 2018 Lei Zhang, Peng Wang, Lingqiao Liu, Chunhua Shen, Wei Wei, Yannning Zhang, Anton Van Den Hengel

Towards this goal, we present a simple but effective two-branch network to simultaneously map semantic descriptions and visual samples into a joint space, on which visual embeddings are forced to regress to their class-level semantic embeddings and the embeddings crossing classes are required to be distinguishable by a trainable classifier.

Zero-Shot Learning

Searching Toward Pareto-Optimal Device-Aware Neural Architectures

no code implementations29 Aug 2018 An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.

Image Classification Language understanding

Semi-supervised Transfer Learning for Image Rain Removal

1 code implementation CVPR 2019 Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, Ying Wu

However, previous deep learning methods need to pre-collect a large set of image pairs with/without synthesized rain for training, which tends to make the neural network be biased toward learning the specific patterns of the synthesized rain, while be less able to generalize to real test samples whose rain types differ from those in the training data.

Single Image Deraining Transfer Learning

DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures

no code implementations ECCV 2018 Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

We propose DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e. g., inference time and memory usage) and device-agnostic (e. g., accuracy and model size) objectives.

Image Classification Language Modelling

Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN

no code implementations10 Jun 2018 Yiqi Yan, Lei Zhang, Jun Li, Wei Wei, Yanning Zhang

Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain.

Super-Resolution

Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

no code implementations5 Jun 2018 Lei Zhang, Peng Wang, Chunhua Shen, Lingqiao Liu, Wei Wei, Yanning Zhang, Anton Van Den Hengel

In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture.

Image Super-Resolution

Video Rain Streak Removal by Multiscale Convolutional Sparse Coding

no code implementations CVPR 2018 Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, Deyu Meng

Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.

Rain Removal

Image Registration Based Flicker Solving in Video Face Replacement and Analysis Based Sub-pixel Image Registration

no code implementations9 Mar 2018 Xiaofang Wang, Guoqiang Xiang, Xinyue Zhang, Wei Wei

In this paper, a framework of video face replacement is proposed and it deals with the flicker of swapped face in video sequence.

Image Registration

A Goal-oriented Neural Conversation Model by Self-Play

no code implementations ICLR 2018 Wei Wei, Quoc V. Le, Andrew M. Dai, Li-Jia Li

One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals.

Language Modelling Language understanding +1

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

Phase Conductor on Multi-layered Attentions for Machine Comprehension

no code implementations ICLR 2018 Rui Liu, Wei Wei, Weiguang Mao, Maria Chikina

Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and self-matching attention model.

Question Answering Reading Comprehension

Should We Encode Rain Streaks in Video as Deterministic or Stochastic?

no code implementations ICCV 2017 Wei Wei, Lixuan Yi, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu

Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks.

Efficient Online Inference for Infinite Evolutionary Cluster models with Applications to Latent Social Event Discovery

no code implementations20 Aug 2017 Wei Wei, Kennth Joseph, Kathleen Carley

The Recurrent Chinese Restaurant Process (RCRP) is a powerful statistical method for modeling evolving clusters in large scale social media data.

Beyond Low Rank: A Data-Adaptive Tensor Completion Method

no code implementations3 Aug 2017 Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton Van Den Hengel, Yanning Zhang

The prior for the non-low-rank structure is established based on a mixture of Gaussians which is shown to be flexible enough, and powerful enough, to inform the completion process for a variety of real tensor data.

When Unsupervised Domain Adaptation Meets Tensor Representations

1 code implementation ICCV 2017 Hao Lu, Lei Zhang, Zhiguo Cao, Wei Wei, Ke Xian, Chunhua Shen, Anton Van Den Hengel

Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.

Unsupervised Domain Adaptation

Learning to Identify Ambiguous and Misleading News Headlines

no code implementations17 May 2017 Wei Wei, Xiaojun Wan

For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies.

Structural Embedding of Syntactic Trees for Machine Comprehension

no code implementations EMNLP 2017 Rui Liu, Junjie Hu, Wei Wei, Zi Yang, Eric Nyberg

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees.

Question Answering Reading Comprehension

A Probabilistic Framework for Location Inference from Social Media

no code implementations23 Feb 2017 Yujie Qian, Jie Tang, Zhilin Yang, Binxuan Huang, Wei Wei, Kathleen M. Carley

In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).

Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior

no code implementations ICCV 2015 Lei Zhang, Wei Wei, Yanning Zhang, Fei Li, Chunhua Shen, Qinfeng Shi

To reconstruct hyperspectral image (HSI) accurately from a few noisy compressive measurements, we present a novel manifold-structured sparsity prior based hyperspectral compressive sensing (HCS) method in this study.

Compressive Sensing

Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity

no code implementations CVPR 2015 Lei Zhang, Wei Wei, Yanning Zhang, Chunna Tian, Fei Li

To address this problem, a novel reweighted Laplace prior based hyperspectral compressive sensing method is proposed in this study.

Compressive Sensing Noise Estimation

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