no code implementations • NAACL (maiworkshop) 2021 • Hao Wu, François Pitie, Gareth Jones
These comments have become a determining factor in the popularity of the videos.
no code implementations • ACL 2022 • Qiang Ning, Ben Zhou, Hao Wu, Haoruo Peng, Chuchu Fan, Matt Gardner
News events are often associated with quantities (e. g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.
no code implementations • 17 Jan 2023 • Bingchen Zhao, Quan Cui, Hao Wu, Osamu Yoshie, Cheng Yang
First, we conduct a benchmark study of representative SSL pre-training methods on large-scale web data in a fair condition.
no code implementations • 21 Dec 2022 • YuAn Liu, Jiacheng Chen, Hao Wu
Learning effective motion features is an essential pursuit of video representation learning.
no code implementations • 15 Dec 2022 • Lars Bengel, Elfia Bezou-Vrakatseli, Lydia Blümel, Federico Castagna, Giulia D'Agostino, Daphne Odekerken, Minal Suresh Patil, Jordan Robinson, Hao Wu, Andreas Xydis
This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI).
no code implementations • 3 Dec 2022 • Jiajia Mi, Hao Wu
We maintain the non-negativity of the model by constructing an augmented Lagrangian function with the ADMM optimization framework.
no code implementations • 17 Oct 2022 • Jinkun Cao, Hao Wu, Kris Kitani
Experiments on video multi-object tracking (MOT) and multi-object tracking and segmentation (MOTS) datasets demonstrate the effectiveness of the proposed DST position encoding.
no code implementations • 4 Oct 2022 • Haoyu Pan, Hao Wu, Tan Yang
In this paper, a modular design is proposed, which decomposes spatiotemporal sequence model into two modules: a spatial encoder-decoder and a predictor.
no code implementations • 12 Sep 2022 • Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, Richard Grisenthwaite, Sangwon Ha, Alexander Heinecke, Patrick Judd, John Kamalu, Naveen Mellempudi, Stuart Oberman, Mohammad Shoeybi, Michael Siu, Hao Wu
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors.
no code implementations • 9 Sep 2022 • Yan Cai, Shijian Li, Wei zhang, Hao Wu, Xu-Ri Yao, Qing Zhao
Hadamard single-pixel imaging (HSI) is an appealing imaging technique due to its features of low hardware complexity and industrial cost.
1 code implementation • COLING 2022 • Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
Limited labeled data increase the risk of distribution shift between test data and training data.
no code implementations • 14 Aug 2022 • Zemiao Peng, Hao Wu
A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service (QoS) data for predicting the unobserved ones with high accuracy.
no code implementations • 8 Aug 2022 • Youyuan Zhang, Jiuniu Wang, Hao Wu, Wenjia Xu
Image captioning models are usually trained according to human annotated ground-truth captions, which could generate accurate but generic captions.
no code implementations • 24 Jun 2022 • Hao Wu, Yongqiang Cheng, Xixi Chen, Zheng Yang, Xiang Li, Hongqiang Wang
These advantages benefit from the geometry of the Toeplitz Hermitian positive definite (HPD) manifold $\mathcal{M}_{\mathcal{T}H_{++}}$, but the sophisticated geometry also results in some challenges for geometric detectors, such as the implementation of the enhanced detector to improve the SCR (signal-to-clutter ratio) and the analysis of the detection performance.
1 code implementation • 4 Jun 2022 • Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, Lin Wang
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
no code implementations • 1 Jun 2022 • Hao Wu, Yifan Miao, Peng Zhang, Yang Tian, Hui Tian
Industrial Internet of Things is an ultra-large-scale system that is much more sophisticated and fragile than conventional industrial platforms.
no code implementations • 30 May 2022 • Siyuan Liang, Hao Wu
Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems.
1 code implementation • 16 May 2022 • Li Yan, Pengcheng Wei, Hong Xie, Jicheng Dai, Hao Wu, Ming Huang
We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph.
no code implementations • Findings (NAACL) 2022 • Xin Wang, Yasheng Wang, Yao Wan, Jiawei Wang, Pingyi Zhou, Li Li, Hao Wu, Jin Liu
Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework.
1 code implementation • ICASSP 2022 • Xiaopeng Ke, Boyu Chang, Hao Wu, Fengyuan Xu, Sheng Zhong
Recently, video summarization (VS) techniques are widely used to alleviate huge processing pressure brought by numerous long videos.
no code implementations • 20 Apr 2022 • Ji Liu, Zheng Xu, Yanmei Zhang, Wei Dai, Hao Wu, Shiping Chen
Since the emergence of blockchain technology, its application in the financial market has always been an area of focus and exploration by all parties.
1 code implementation • 12 Apr 2022 • Yang Li, Ji Chen, Fu Li, Boxun Fu, Hao Wu, Youshuo Ji, Yijin Zhou, Yi Niu, Guangming Shi, Wenming Zheng
GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.
no code implementations • 16 Mar 2022 • Ling Guo, Hao Wu, Xiaochen Yu, Tao Zhou
We introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional partial differential equations (FPDEs).
no code implementations • 14 Mar 2022 • Hao Wu, Ming Tang
Here, we propose and experimentally demonstrate an OTDR deconvolution neural network based on deep convolutional neural networks.
no code implementations • Findings (ACL) 2022 • Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering.
1 code implementation • 17 Dec 2021 • Quan Cui, Boyan Zhou, Yu Guo, Weidong Yin, Hao Wu, Osamu Yoshie, Yubo Chen
However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.
1 code implementation • 28 Oct 2021 • Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, Nathan Kutz, Steven L. Brunton, Frank Noé
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
1 code implementation • NeurIPS 2021 • BoWen Zhang, Yidong Wang, Wenxin Hou, Hao Wu, Jindong Wang, Manabu Okumura, Takahiro Shinozaki
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
no code implementations • 13 Oct 2021 • Jiyao Liu, Yanxi Zhao, Hao Wu, Dongmei Jiang
The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features.
no code implementations • 12 Oct 2021 • Rongyao Wang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Hao Wu, Qian Zhang
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
no code implementations • 9 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.
no code implementations • 30 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.
no code implementations • 11 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.
no code implementations • 10 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.
no code implementations • ICLR Workshop EBM 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.
no code implementations • NeurIPS 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.
no code implementations • 19 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.
no code implementations • 7 Apr 2021 • Qixuan Wang, Hao Wu
Cells and microorganisms adopt various strategies to migrate in response to different environmental stimuli.
no code implementations • 31 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.
1 code implementation • SEMEVAL 2021 • Yuxin Jiang, Ziyi Shou, Qijun Wang, Hao Wu, Fangzhen Lin
This paper presents our submitted system to SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.
no code implementations • NAACL (DLG4NLP) 2022 • Irene Li, Aosong Feng, Hao Wu, Tianxiao Li, Toyotaro Suzumura, Ruihai Dong
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
no code implementations • 3 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
1 code implementation • 1 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.
no code implementations • 5 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
no code implementations • 4 Feb 2021 • Lizhi Sun, Shuocheng Wang, Hao Wu, Yuhang Gong, Fengyuan Xu, Yunxin Liu, Hao Han, Sheng Zhong
ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices for secure execution.
Cryptography and Security
no code implementations • 29 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
no code implementations • 1 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.
no code implementations • SEMEVAL 2020 • Yuhang Wu, Hao Wu
This paper describes our system in subtask A of SemEval 2020 Shared Task 4.
1 code implementation • 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.
no code implementations • 1 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.
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.
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.
no code implementations • EMNLP 2020 • Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasovi{\'c}, Zhen Nie
High-quality and large-scale data are key to success for AI systems.
no code implementations • 15 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.
no code implementations • 25 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.
no code implementations • 23 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
no code implementations • 5 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.
2 code implementations • 4 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.
no code implementations • 6 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.
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.
Ranked #2 on
Question Answering
on Torque
1 code implementation • 20 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.
3 code implementations • 20 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.
no code implementations • 3 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.
no code implementations • 17 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.
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.
no code implementations • 28 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.
no code implementations • 20 Dec 2019 • Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu, Qingwei Guo, Qiaobo Chen, Yinyuting Yin, Hao Zhang, Tengfei Shi, Liang Wang, Qiang Fu, Wei Yang, Lanxiao Huang
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.
1 code implementation • 16 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
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.
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.
no code implementations • 25 Sep 2019 • Hui Shi, Yang Zhang, Hao Wu, Shiyu Chang, Kaizhi Qian, Mark Hasegawa-Johnson, Jishen Zhao
Convolutional neural network (CNN) for time series data implicitly assumes that the data are uniformly sampled, whereas many event-based and multi-modal data are nonuniform or have heterogeneous sampling rates.
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.
no code implementations • 22 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.
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.
no code implementations • 19 Jun 2019 • Jingyu Yang, Ji Xu, Kun Li, Yu-Kun Lai, Huanjing Yue, Jianzhi Lu, Hao Wu, Yebin Liu
This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes.
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.
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.
1 code implementation • CVPR 2019 • Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, Wei-Ying Ma
We propose the Unified Visual-Semantic Embeddings (Unified VSE) for learning a joint space of visual representation and textual semantics.
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.
no code implementations • 26 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.
no code implementations • 16 May 2019 • Hao Wu, Xiangrong Xu, Wenbin Gao
Surface defect inspection based on machine vision is often affected by uneven illumination.
no code implementations • 5 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.
no code implementations • 23 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.
1 code implementation • 11 Apr 2019 • Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei LI, Weiwei Sun, Wei-Ying Ma
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
2 code implementations • 4 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.
no code implementations • 28 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.
no code implementations • 14 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.
no code implementations • WS 2018 • Qimin Zhou, Hao Wu
This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}.
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.
no code implementations • SEMEVAL 2018 • Zhengxin Zhang, Qimin Zhou, Hao Wu
We participate in two subtasks for English tweets: EI-reg and V-reg.
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.
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.
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.
no code implementations • 10 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.
no code implementations • 6 Apr 2018 • Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
no code implementations • 6 Feb 2018 • Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng
Estimating the travel time of a path is of great importance to smart urban mobility.
no code implementations • 1 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.
3 code implementations • CVPR 2018 • Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, Edward Choi
We present MorphNet, an approach to automate the design of neural network structures.
1 code implementation • 16 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.
8 code implementations • ICLR 2018 • Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
no code implementations • 7 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.
no code implementations • SEMEVAL 2017 • Hao Wu, He-Yan Huang, Ping Jian, Yuhang Guo, Chao Su
This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task.
no code implementations • 14 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.
no code implementations • 27 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.
no code implementations • EMNLP 2018 • John P. Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu
We examine the impact of a test set question's difficulty to determine if there is a relationship between difficulty and performance.
no code implementations • 20 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.
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.
no code implementations • 28 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.
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).
no code implementations • 22 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.
no code implementations • 23 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.
no code implementations • 19 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.
no code implementations • 14 Apr 2015 • Hao Wu, Yi Wan
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied.
no code implementations • 31 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.
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.