Search Results for author: Fan Zhou

Found 66 papers, 27 papers with code

GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting

no code implementations18 Jun 2024 Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, James Zhang, Jun Zhou, Hongyuan Mei, Weitao Lin, Zi Zhuang, Wenxin Ning, Yunhua Hu, Siqiao Xue

These methods merely take the temporal hierarchical structure to maintain coherence without improving the forecasting accuracy.

OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

1 code implementation18 Jun 2024 Zhen Huang, Zengzhi Wang, Shijie Xia, Xuefeng Li, Haoyang Zou, Ruijie Xu, Run-Ze Fan, Lyumanshan Ye, Ethan Chern, Yixin Ye, Yikai Zhang, Yuqing Yang, Ting Wu, Binjie Wang, Shichao Sun, Yang Xiao, Yiyuan Li, Fan Zhou, Steffi Chern, Yiwei Qin, Yan Ma, Jiadi Su, Yixiu Liu, Yuxiang Zheng, Shaoting Zhang, Dahua Lin, Yu Qiao, PengFei Liu

We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions.

The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations

no code implementations27 Mar 2024 Fan Zhou

This study explores the distinctions between neural machine translation (NMT) and human translation (HT) through the lens of translation relations.

Machine Translation NMT +2

Prediction of Translation Techniques for the Translation Process

no code implementations21 Mar 2024 Fan Zhou, Vincent Vandeghinste

Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations.

Machine Translation Translation

Dissecting Human and LLM Preferences

1 code implementation17 Feb 2024 Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, PengFei Liu

As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation.

Language Modelling Large Language Model

Generalizing across Temporal Domains with Koopman Operators

no code implementations12 Feb 2024 Qiuhao Zeng, Wei Wang, Fan Zhou, Gezheng Xu, Ruizhi Pu, Changjian Shui, Christian Gagne, Shichun Yang, Boyu Wang, Charles X. Ling

By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains.

Domain Generalization Generalization Bounds

Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference

1 code implementation IEEE Transactions on Big Data 2023 Xovee Xu, Zhiyuan Wang, Qiang Gao, Ting Zhong, Bei Hui, Fan Zhou, Goce Trajcevski

Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs.

Fine-Grained Urban Flow Inference Image Super-Resolution

Hessian Aware Low-Rank Weight Perturbation for Continual Learning

1 code implementation26 Nov 2023 Jiaqi Li, Rui Wang, Yuanhao Lai, Changjian Shui, Sabyasachi Sahoo, Charles X. Ling, Shichun Yang, Boyu Wang, Christian Gagné, Fan Zhou

We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method.

Continual Learning

OpenAgents: An Open Platform for Language Agents in the Wild

2 code implementations16 Oct 2023 Tianbao Xie, Fan Zhou, Zhoujun Cheng, Peng Shi, Luoxuan Weng, Yitao Liu, Toh Jing Hua, Junning Zhao, Qian Liu, Che Liu, Leo Z. Liu, Yiheng Xu, Hongjin Su, Dongchan Shin, Caiming Xiong, Tao Yu

Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).

2D Object Detection

Lemur: Harmonizing Natural Language and Code for Language Agents

1 code implementation10 Oct 2023 Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, Tao Yu

We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.

Deep Optimal Timing Strategies for Time Series

1 code implementation9 Oct 2023 Chen Pan, Fan Zhou, Xuanwei Hu, Xinxin Zhu, Wenxin Ning, Zi Zhuang, Siqiao Xue, James Zhang, Yunhua Hu

Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data.

Probabilistic Time Series Forecasting Time Series

Continuous Invariance Learning

no code implementations9 Oct 2023 Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jiameng Liu, Yansu He, Yuan Yuan, Yu Liu, James Zhang, Yujiu Yang, Hao Wang

To address this challenge, we then propose Continuous Invariance Learning (CIL), which extracts invariant features across continuously indexed domains.

Cloud Computing

Variance Control for Distributional Reinforcement Learning

1 code implementation30 Jul 2023 Qi Kuang, Zhoufan Zhu, Liwen Zhang, Fan Zhou

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, very few studies investigate the validity of the obtained Q-function estimator in the distributional setting.

Distributional Reinforcement Learning reinforcement-learning

EasyTPP: Towards Open Benchmarking Temporal Point Processes

1 code implementation16 Jul 2023 Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei

In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.

Benchmarking Point Processes

Multi-modal Representation Learning for Social Post Location Inference

no code implementations11 Jun 2023 Ruiting Dai, Jiayi Luo, Xucheng Luo, Lisi Mo, Wanlun Ma, Fan Zhou

Inferring geographic locations via social posts is essential for many practical location-based applications such as product marketing, point-of-interest recommendation, and infector tracking for COVID-19.

Marketing Representation Learning +1

From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning

1 code implementation17 Apr 2023 Qian Liu, Fan Zhou, Zhengbao Jiang, Longxu Dou, Min Lin

Empirical results on various benchmarks validate that the integration of SQL execution leads to significant improvements in zero-shot scenarios, particularly in table reasoning.

Zero-shot Generalization

Urban Regional Function Guided Traffic Flow Prediction

no code implementations17 Mar 2023 Kuo Wang, Lingbo Liu, Yang Liu, Guanbin Li, Fan Zhou, Liang Lin

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest.

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

no code implementations11 Feb 2023 Fan Zhou, Chen Pan, Lintao Ma, Yu Liu, Shiyu Wang, James Zhang, Xinxin Zhu, Xuanwei Hu, Yunhua Hu, Yangfei Zheng, Lei Lei, Yun Hu

Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only, we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e. g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks.

Decision Making Multivariate Time Series Forecasting +1

Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment

1 code implementation19 Jan 2023 Qiuhao Zeng, Wei Wang, Fan Zhou, Charles Ling, Boyu Wang

In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment.

Data Augmentation Evolving Domain Generalization +1

Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization

no code implementations5 Jan 2023 Chengchun Shi, Zhengling Qi, Jianing Wang, Fan Zhou

When the initial policy is consistent, under some mild conditions, our method will yield a policy whose value converges to the optimal one at a faster rate than the initial policy, achieving the desired ``value enhancement" property.

Decision Making reinforcement-learning +1

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

1 code implementation28 Dec 2022 Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yangfei Zheng, Bo Zheng, Lei Lei, Yun Hu

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i. e., the forecasts should satisfy the hierarchical aggregation constraints.

Multivariate Time Series Forecasting Time Series

A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability

1 code implementation21 Dec 2022 Chengtai Cao, Fan Zhou, Yurou Dai, JianPing Wang, Kunpeng Zhang

We begin by introducing a novel taxonomy that categorizes MixDA into Mixup-based, Cutmix-based, and mixture approaches based on a hierarchical perspective of the data mixing operation.

Computational Efficiency Data Augmentation

Predicting Human Mobility via Self-supervised Disentanglement Learning

no code implementations17 Nov 2022 Qiang Gao, Jinyu Hong, Xovee Xu, Ping Kuang, Fan Zhou, Goce Trajcevski

However, most of the existing research concentrates on fusing different semantics underlying sequential trajectories for mobility pattern learning which, in turn, yields a narrow perspective on comprehending human intrinsic motions.

Disentanglement

Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs

no code implementations28 Oct 2022 Qiang Gao, Xinzhu Zhou, Kunpeng Zhang, Li Huang, Siyuan Liu, Fan Zhou

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns.

Decision Making

Evolving Domain Generalization

no code implementations31 May 2022 William Wei Wang, Gezheng Xu, Ruizhi Pu, Jiaqi Li, Fan Zhou, Changjian Shui, Charles Ling, Christian Gagné, Boyu Wang

Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.

Evolving Domain Generalization Meta-Learning

TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data

1 code implementation25 May 2022 Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Shi Han, Dongmei Zhang

Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.

Question Answering

Automatic Registration of Images with Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Removal

no code implementations2 Apr 2022 Ming Zhao, Yongpeng Wu, Shengda Pan, Fan Zhou, Bowen An, André Kaup

This new approach is designed to address the problems associated with the registration of images with affine deformations and inconsistent content, such as remote sensing images with different spectral content or noise interference, or map images with inconsistent annotations.

Image Registration

Gap Minimization for Knowledge Sharing and Transfer

no code implementations26 Jan 2022 Boyu Wang, Jorge Mendez, Changjian Shui, Fan Zhou, Di wu, Gezheng Xu, Christian Gagné, Eric Eaton

Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e. g., $\mathcal{H}$-divergence or discrepancy distance), we theoretically show that the performance gap can be viewed as a data- and algorithm-dependent regularizer, which controls the model complexity and leads to finer guarantees.

Representation Learning Transfer Learning

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

no code implementations24 Jan 2022 Haoyu Dong, Zhoujun Cheng, Xinyi He, Mengyu Zhou, Anda Zhou, Fan Zhou, Ao Liu, Shi Han, Dongmei Zhang

Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and images, and they have achieved new state-of-the-arts on various tasks such as table question answering, table type recognition, column relation classification, table search, formula prediction, etc.

Denoising Question Answering +2

Rate-Optimal Subspace Estimation on Random Graphs

no code implementations NeurIPS 2021 Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang

We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph.

Directional Domain Generalization

no code implementations29 Sep 2021 Wei Wang, Jiaqi Li, Ruizhi Pu, Gezheng Xu, Fan Zhou, Changjian Shui, Charles Ling, Boyu Wang

Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data.

Domain Generalization Meta-Learning +1

CCGL: Contrastive Cascade Graph Learning

1 code implementation27 Jul 2021 Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu

Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.

Data Augmentation Graph Learning +3

Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning

no code implementations14 May 2021 Fan Zhou, Zhoufan Zhu, Qi Kuang, Liwen Zhang

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address.

Atari Games Distributional Reinforcement Learning +3

Multi-task Learning by Leveraging the Semantic Information

no code implementations3 Mar 2021 Fan Zhou, Brahim Chaib-Draa, Boyu Wang

To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions.

Multi-Task Learning

Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms

1 code implementation11 Feb 2021 Fan Zhou, Shikai Luo, XiaoHu Qie, Jieping Ye, Hongtu Zhu

How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.

Optimization and Control Applications

Embedding Symbolic Temporal Knowledge into Deep Sequential Models

no code implementations28 Jan 2021 Yaqi Xie, Fan Zhou, Harold Soh

However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction.

Action Recognition Graph Neural Network +3

Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs

no code implementations1 Jan 2021 Fan Zhou, Yifeng Pan, Shenghua Zhu, Xin He

Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines.

reinforcement-learning Reinforcement Learning (RL)

Non-Crossing Quantile Regression for Distributional Reinforcement Learning

no code implementations NeurIPS 2020 Fan Zhou, Jianing Wang, Xingdong Feng

Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs.

Atari Games Distributional Reinforcement Learning +3

Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence

no code implementations30 Jul 2020 Changjian Shui, Qi Chen, Jun Wen, Fan Zhou, Christian Gagné, Boyu Wang

We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.

Domain Adaptation Transfer Learning

Domain Generalization via Optimal Transport with Metric Similarity Learning

no code implementations21 Jul 2020 Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang, Brahim Chaib-Draa

Previous domain generalization approaches mainly focused on learning invariant features and stacking the learned features from each source domain to generalize to a new target domain while ignoring the label information, which will lead to indistinguishable features with an ambiguous classification boundary.

Domain Generalization Metric Learning

Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder

no code implementations ACL 2020 Fan Zhou, Shengming Zhang, Yi Yang

To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC).

General Classification Management +3

Interpreting Twitter User Geolocation

no code implementations ACL 2020 Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang

Identifying user geolocation in online social networks is an essential task in many location-based applications.

Discriminative Active Learning for Domain Adaptation

no code implementations24 May 2020 Fan Zhou, Changjian Shui, Bincheng Huang, Boyu Wang, Brahim Chaib-Draa

To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation.

Active Learning Domain Adaptation

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

3 code implementations22 May 2020 Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang

The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.

Feature Engineering Marketing

Metric learning by Similarity Network for Deep Semi-Supervised Learning

no code implementations29 Apr 2020 Sanyou Wu, Xingdong Feng, Fan Zhou

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning.

Metric Learning

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

1 code implementation26 Mar 2020 Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, Kunpeng Zhang

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.

Graph Neural Network

Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay

no code implementations22 Mar 2020 Fan Zhou, Chengtai Cao

Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks.

Graph Classification Graph Learning +1

Relational State-Space Model for Stochastic Multi-Object Systems

no code implementations ICLR 2020 Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.

Object Time Series +1

Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

1 code implementation NeurIPS 2019 Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.

General Classification Imputation +1

Deep Active Learning: Unified and Principled Method for Query and Training

1 code implementation20 Nov 2019 Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning.

Active Learning

A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model

no code implementations5 Nov 2019 Fan Zhou, Ping Li

Let $\mathbf{x}_j = \mathbf{\theta} + \mathbf{\epsilon}_j$, $j=1,\dots, n$ be i. i. d.

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

1 code implementation10 Jun 2019 Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang

We define two types of entropy-based metrics, i. e. (1) the discarding of pixel-wise information used in the forward propagation, and (2) the uncertainty of the input reconstruction, to measure input information contained by a specific layer from two perspectives.

Fairness

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

1 code implementation23 May 2019 Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.

Few-Shot Learning General Classification +1

A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction

no code implementations12 Mar 2019 Fan Zhou, Guojing Cong

Reducing communication in training large-scale machine learning applications on distributed platform is still a big challenge.

Avg

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

no code implementations3 Dec 2018 Kunal Sankhe, Mauro Belgiovine, Fan Zhou, Shamnaz Riyaz, Stratis Ioannidis, Kaushik Chowdhury

This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.

Classification General Classification

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Trajectory-User Linking via Variational AutoEncoder

1 code implementation International Joint Conference on Artificial Intelligence 2018 Fan Zhou, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, Fengli Zhang

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification.

Nonparametric Estimation of Low Rank Matrix Valued Function

no code implementations17 Feb 2018 Fan Zhou

We also propose another new estimator based on bias-reducing kernels to study the case when $A$ is not necessarily low rank and establish an upper bound on its risk measured by $L_{\infty}$-norm.

Matrix Completion Model Selection

On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization

no code implementations3 Aug 2017 Fan Zhou, Guojing Cong

We establish the convergence results of K-AVG for nonconvex objectives and explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is a special case of K-AVG with $K=1$.

Avg

The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising

no code implementations5 Jul 2017 Dong Xia, Fan Zhou

In addition, the bounds established for HOSVD also elaborate the one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced (or fat) matrices.

Clustering Denoising

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