Search Results for author: Hao Wu

Found 85 papers, 17 papers with code

Enabling variable high spatial resolution retrieval from a long pulse BOTDA sensor

no code implementations9 Sep 2021 Zhao Ge, Li Shen, Can Zhao, Hao Wu, Zhiyong Zhao, Ming Tang

We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses.

Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models

no code implementations30 Aug 2021 Ling Guo, Hao Wu, Tao Zhou

We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements.

Gaussian Processes

Cooperative Learning for Noisy Supervision

no code implementations11 Aug 2021 Hao Wu, Jiangchao Yao, Ya zhang, Yanfeng Wang

Learning with noisy labels has gained the enormous interest in the robust deep learning area.

Learning with noisy labels

SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation

no code implementations10 Aug 2021 Xin Wang, Yasheng Wang, Fei Mi, Pingyi Zhou, Yao Wan, Xiao Liu, Li Li, Hao Wu, Jin Liu, Xin Jiang

Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.

Clone Detection Code Search +5

Conjugate Energy-Based Models

no code implementations25 Jun 2021 Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent

In this paper, we propose conjugate energy-based models (CEBMs), a new class of energy-based models that define a joint density over data and latent variables.

Nested Variational Inference

no code implementations21 Jun 2021 Heiko Zimmermann, Hao Wu, Babak Esmaeili, Jan-Willem van de Meent

We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting.

Variational Inference

Boosting Offline Reinforcement Learning with Residual Generative Modeling

no code implementations19 Jun 2021 Hua Wei, Deheng Ye, Zhao Liu, Hao Wu, Bo Yuan, Qiang Fu, Wei Yang, Zhenhui Li

While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.

Offline RL Q-Learning

Mathematical Modeling of Chemotaxis Guided Amoeboid Cell Swimming

no code implementations7 Apr 2021 Qixuan Wang, Hao Wu

Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli.

Collaborative Label Correction via Entropy Thresholding

no code implementations31 Mar 2021 Hao Wu, Jiangchao Yao, Jiajie Wang, Yinru Chen, Ya zhang, Yanfeng Wang

Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels.

Minimization of ion micromotion with artificial neural network

no code implementations3 Mar 2021 Yang Liu, Qi-feng Lao, Peng-fei Lu, Xin-xin Rao, Hao Wu, Teng Liu, Kun-xu Wang, Zhao Wang, Ming-shen Li, Feng Zhu, Luo Le

Minimizing the micromotion of the single trapped ion in a linear Paul trap is a tedious and time-consuming work, but is of great importance in cooling the ion into the motional ground state as well as maintaining long coherence time, which is crucial for quantum information processing and quantum computation.

Atomic Physics Quantum Physics

Learning Proposals for Probabilistic Programs with Inference Combinators

1 code implementation1 Mar 2021 Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent

Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives.

A Convergent Semi-Proximal Alternating Direction Method of Multipliers for Recovering Internet Traffics from Link Measurements

no code implementations5 Feb 2021 Zhenyu Ming, Liping Zhang, Hao Wu, Yanwei Xu, Mayank Bakshi, Bo Bai, Gong Zhang

Our model can be divided into a series of subproblems, which only relate to the traffics in a certain individual time interval.

Optimization and Control

App Developer Centric Trusted Execution Environment

no code implementations4 Feb 2021 Lizhi Sun, Shuocheng Wang, Hao Wu, Yuhang Gong, Fengyuan Xu, Yunxin Liu, Hao Han, Sheng Zhong

ARM TrustZone is the de-facto hardware TEE implementation on mobile devices like smartphones.

Cryptography and Security

Coalition Game Based Full-duplex Popular Content Distribution in mmWave Vehicular Networks

no code implementations29 Jan 2021 Yibing Wang, Hao Wu, Yong Niu, Zhu Han, Bo Ai, Zhangdui Zhong

We evaluate the proposed scheme by extensive simulations in mmWave vehicular networks.

Fairness Information Theory Networking and Internet Architecture Information Theory

Adaptive Tree Wasserstein Minimization for Hierarchical Generative Modeling

no code implementations1 Jan 2021 ZiHao Wang, Xu Zhao, Tam Le, Hao Wu, Yong Zhang, Makoto Yamada

In this work, we consider OT over tree metrics, which is more general than the sliced Wasserstein and includes the sliced Wasserstein as a special case, and we propose a fast minimization algorithm in $O(n)$ for the optimal Wasserstein-1 transport plan between two distributions in the tree structure.

Unsupervised Domain Adaptation

Learning the Best Pooling Strategy for Visual Semantic Embedding

no code implementations CVPR 2021 Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, Changhu Wang

Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions.

Video-Text Retrieval

Memory Group Sampling Based Online Action Recognition Using Kinetic Skeleton Features

no code implementations1 Nov 2020 Guoliang Liu, Qinghui Zhang, Yichao Cao, Junwei Li, Hao Wu, Guohui Tian

First, we combine the spatial and temporal skeleton features to depict the actions, which include not only the geometrical features, but also multi-scale motion features, such that both the spatial and temporal information of the action are covered.

Action Recognition

Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction

1 code implementation EMNLP 2020 Xu Zhao, ZiHao Wang, Hao Wu, Yong Zhang

In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment.

Bilingual Lexicon Induction

A Relaxed Matching Procedure for Unsupervised BLI

no code implementations ACL 2020 Xu Zhao, ZiHao Wang, Hao Wu, Yong Zhang

Recently unsupervised Bilingual Lexicon Induction (BLI) without any parallel corpus has attracted much research interest.

Bilingual Lexicon Induction

Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm

no code implementations15 Sep 2020 Li Shen, Zhiyong Zhao, Can Zhao, Hao Wu, Chao Lu, Ming Tang

The frequency dependency of Brillouin gain temporal envelope is investigated by simulation, and its impact on the recovered results of deconvolution algorithm is thoroughly analyzed.


DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors

no code implementations25 Jun 2020 Wenbin Gao, Lei Zhang, Qi Teng, Jun He, Hao Wu

Recently, two attention methods are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) network, which can capture the dependencies of sensing signals in both spatial and temporal domains simultaneously.

Activity Recognition

Learning Based Distributed Tracking

no code implementations23 Jun 2020 Hao Wu, Junhao Gan, Rui Zhang

Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution.

Data Structures and Algorithms

Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices

no code implementations5 Jun 2020 Xin Cheng, Lei Zhang, Yin Tang, Yue Liu, Hao Wu, Jun He

For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.

Activity Recognition

Response to LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts

2 code implementations4 Jun 2020 Hao Wu, Gareth J. F. Jones, Francois Pitie

Recently the Chinese video sharing platform Bilibili, has popularised a novel captioning system where user comments are displayed as streams of moving subtitles overlaid on the video playback screen and broadcast to all viewers in real-time.

Modeling nanoconfinement effects using active learning

no code implementations6 May 2020 Javier E. Santos, Mohammed Mehana, Hao Wu, Masa Prodanovic, Michael J. Pyrcz, Qinjun Kang, Nicholas Lubbers, Hari Viswanathan

At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions.

Active Learning

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

no code implementations EMNLP 2020 Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, Dan Roth

A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.

Machine Reading Comprehension

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

no code implementations20 Apr 2020 Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev, Paulius Micikevicius

Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions.


Masked Face Recognition Dataset and Application

3 code implementations20 Mar 2020 Zhongyuan Wang, Guangcheng Wang, Baojin Huang, Zhangyang Xiong, Qi Hong, Hao Wu, Peng Yi, Kui Jiang, Nanxi Wang, Yingjiao Pei, Heling Chen, Yu Miao, Zhibing Huang, Jinbi Liang

These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed.


Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN

no code implementations3 Mar 2020 Hao Wu, Jan Paul Siebert, Xiangrong Xu

This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method.

Object Detection

DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories

no code implementations17 Feb 2020 Hao Wu, Hanyuan Zhang, Xin-Yu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang

We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.

Stochastic Normalizing Flows

1 code implementation NeurIPS 2020 Hao Wu, Jonas Köhler, Frank Noé

The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics.

Structural-Aware Sentence Similarity with Recursive Optimal Transport

no code implementations28 Jan 2020 Zihao Wang, Yong Zhang, Hao Wu

Moreover, we further develop Recursive Optimal Similarity (ROTS) for sentences with the valuable semantic insights from the connections between cosine similarity of weighted average of word vectors and optimal transport.

Sentence Similarity

Deep learning Markov and Koopman models with physical constraints

1 code implementation16 Dec 2019 Andreas Mardt, Luca Pasquali, Frank Noé, Hao Wu

Here we develop theory and methods for deep learning Markov and Koopman models that can bear such physical constraints.

Computational Physics

Structured Multi-Hashing for Model Compression

no code implementations CVPR 2020 Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan

Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.

Model Compression

Amortized Population Gibbs Samplers with Neural Sufficient Statistics

1 code implementation ICML 2020 Hao Wu, Heiko Zimmermann, Eli Sennesh, Tuan Anh Le, Jan-Willem van de Meent

We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling.

Variational Inference

Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds

1 code implementation IJCNLP 2019 John P. Lalor, Hao Wu, Hong Yu

We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods.

Natural Language Inference Sentiment Analysis

An encoding framework with brain inner state for natural image identification

no code implementations22 Aug 2019 Hao Wu, Ziyu Zhu, Jiayi Wang, Nanning Zheng, Badong Chen

The framework comprises two parts: forward encoding model that deals with visual stimuli and inner state model that captures influence from intrinsic connections in the brain.

Brain Decoding

Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes

no code implementations ICCV 2019 Hao Yang, Hao Wu, Hao Chen

However, these methods require fully annotated object bounding boxes for training, which are incredibly hard to scale up due to the high annotation cost.

Object Detection Re-Ranking

Joint Reasoning for Temporal and Causal Relations

no code implementations ACL 2018 Qiang Ning, Zhili Feng, Hao Wu, Dan Roth

Understanding temporal and causal relations between events is a fundamental natural language understanding task.

Natural Language Understanding

A Variational Approach for Learning from Positive and Unlabeled Data

1 code implementation NeurIPS 2020 Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu

Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.

Fraud Detection Text Classification

ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining

no code implementations SEMEVAL 2019 Qimin Zhou, Zhengxin Zhang, Hao Wu, Linmao Wang

In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model.

Adaptive Learning Material Recommendation in Online Language Education

no code implementations26 May 2019 Shuhan Wang, Hao Wu, Ji Hun Kim, Erik Andersen

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials.

Uneven illumination surface defects inspection based on convolutional neural network

no code implementations16 May 2019 Hao Wu, Xiangrong Xu, Wenbin Gao

Surface defect inspection based on machine vision is often affected by uneven illumination.

Free Component Analysis: Theory, Algorithms & Applications

no code implementations5 May 2019 Hao Wu, Raj Rao Nadakuditi

We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures.

Wasserstein-Fisher-Rao Document Distance

no code implementations23 Apr 2019 Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao

As a fundamental problem of natural language processing, it is important to measure the distance between different documents.

Semantic Similarity Semantic Textual Similarity

Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

2 code implementations4 Dec 2018 Frank Noé, Simon Olsson, Jonas Köhler, Hao Wu

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge.

Variational Selection of Features for Molecular Kinetics

no code implementations28 Nov 2018 Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé

The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.

Model Selection

Composing Modeling and Inference Operations with Probabilistic Program Combinators

no code implementations14 Nov 2018 Eli Sennesh, Adam Ścibior, Hao Wu, Jan-Willem van de Meent

We assume that models are dynamic, but that model composition is static, in the sense that combinator application takes place prior to evaluating the model on data.

Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

no code implementations SEMEVAL 2018 Zewen Chi, He-Yan Huang, Jiangui Chen, Hao Wu, Ran Wei

This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets.

Sentence Classification Sentiment Analysis

Deep Generative Markov State Models

2 code implementations NeurIPS 2018 Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe

We propose a deep generative Markov State Model (DeepGenMSM) learning framework for inference of metastable dynamical systems and prediction of trajectories.

Time Series

A Multi-Axis Annotation Scheme for Event Temporal Relations

no code implementations ACL 2018 Qiang Ning, Hao Wu, Dan Roth

Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.

Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource

no code implementations NAACL 2018 Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth

We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow.

Relation Extraction

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

no code implementations10 Apr 2018 Hao Yu, Zhaoning Zhang, Zheng Qin, Hao Wu, Dongsheng Li, Jun Zhao, Xicheng Lu

LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.

A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections

no code implementations1 Dec 2017 Siyu Yu, Nanning Zheng, Yongqiang Ma, Hao Wu, Badong Chen

Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals.

Brain Decoding

VAMPnets: Deep learning of molecular kinetics

1 code implementation16 Oct 2017 Andreas Mardt, Luca Pasquali, Hao Wu, Frank Noé

There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations.

Dimensionality Reduction

Network Vector: Distributed Representations of Networks with Global Context

no code implementations7 Sep 2017 Hao Wu, Kristina Lerman

We propose a neural embedding algorithm called Network Vector, which learns distributed representations of nodes and the entire networks simultaneously.

General Classification Node Classification

Variational approach for learning Markov processes from time series data

no code implementations14 Jul 2017 Hao Wu, Frank Noé

This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model.

Model Selection Time Series

Soft Label Memorization-Generalization for Natural Language Inference

no code implementations27 Feb 2017 John P. Lalor, Hao Wu, Hong Yu

Often when multiple labels are obtained for a training example it is assumed that there is an element of noise that must be accounted for.

Natural Language Inference

Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations

no code implementations20 Oct 2016 Hao Wu, Feliks Nüske, Fabian Paul, Stefan Klus, Peter Koltai, Frank Noé

Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters.

Dimensionality Reduction

Spectral learning of dynamic systems from nonequilibrium data

no code implementations NeurIPS 2016 Hao Wu, Frank Noé

Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems.

Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

no code implementations28 Jun 2016 Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, Narayan Bhamidipati

In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them.

Building an Evaluation Scale using Item Response Theory

no code implementations EMNLP 2016 John P. Lalor, Hao Wu, Hong Yu

Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1).

Natural Language Inference

Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data

no code implementations22 Feb 2016 Hao Wu, Xinwei Deng, Naren Ramakrishnan

Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses.

The Computational Principles of Learning Ability

no code implementations23 Sep 2015 Hao Wu

It has been quite a long time since AI researchers in the field of computer science stop talking about simulating human intelligence or trying to explain how brain works.

Common Sense Reasoning

What is Learning? A primary discussion about information and Representation

no code implementations19 May 2015 Hao Wu

While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning.

Clustering Assisted Fundamental Matrix Estimation

no code implementations14 Apr 2015 Hao Wu, Yi Wan

In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied.

3D Reconstruction

Maximum Margin Clustering for State Decomposition of Metastable Systems

no code implementations31 Dec 2014 Hao Wu

When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states.

ILLINOISCLOUDNLP: Text Analytics Services in the Cloud

no code implementations LREC 2014 Hao Wu, Zhiye Fei, Aaron Dai, Mark Sammons, Dan Roth, Stephen Mayhew

Natural Language Processing (NLP) continues to grow in popularity in a range of research and commercial applications.

Knowledge Base Population

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