Search Results for author: Gang Chen

Found 122 papers, 42 papers with code

Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling

1 code implementation COLING 2022 Yangjun Wu, Han Wang, Dongxiang Zhang, Gang Chen, Hao Zhang

Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance.

Intent Detection Question Answering +3

SkipBERT: Efficient Inference with Shallow Layer Skipping

1 code implementation ACL 2022 Jue Wang, Ke Chen, Gang Chen, Lidan Shou, Julian McAuley

In this paper, we propose SkipBERT to accelerate BERT inference by skipping the computation of shallow layers.

Optimizing Large Model Training through Overlapped Activation Recomputation

no code implementations13 Jun 2024 Ping Chen, Wenjie Zhang, Shuibing He, Yingjie Gu, Zhuwei Peng, Kexin Huang, Xuan Zhan, Weijian Chen, Yi Zheng, Zhefeng Wang, Yanlong Yin, Gang Chen

Our comprehensive evaluation using GPT models with 1. 3B-20B parameters shows that both OPT and HEU outperform the state-of-the-art recomputation approaches (e. g., Megatron-LM and Checkmake) by 1. 02-1. 53x.

Processing, evaluating and understanding FMRI data with

no code implementations7 Jun 2024 Richard C. Reynolds, Daniel R. Glen, Gang Chen, Ziad S. Saad, Robert W. Cox, Paul A. Taylor

All of these features help users evaluate and understand their data and processing in detail.

ADBA:Approximation Decision Boundary Approach for Black-Box Adversarial Attacks

1 code implementation7 Jun 2024 Feiyang Wang, Xingquan Zuo, Hai Huang, Gang Chen

Many machine learning models are susceptible to adversarial attacks, with decision-based black-box attacks representing the most critical threat in real-world applications.

Revisiting CNNs for Trajectory Similarity Learning

1 code implementation30 May 2024 Zhihao Chang, Linzhu Yu, Huan Li, Sai Wu, Gang Chen, Dongxiang Zhang

To mitigate the computational burden for long trajectories, neural networks have been widely employed for similarity learning and each trajectory is encoded as a high-dimensional vector for similarity search with linear complexity.

Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection

no code implementations28 May 2024 Zhengji Li, Xi Xiao, Jiacheng Xie, Yuxiao Fan, Wentao Wang, Gang Chen, Liqiang Zhang, Tianyang Wang

Due to a substantial difference between the images generated by CycleGAN and real road images, we proposed a data enhancement method based on an improved Scharr filter, CycleGAN, and Laplacian pyramid.

Data Contamination Calibration for Black-box LLMs

1 code implementation20 May 2024 Wentao Ye, Jiaqi Hu, Liyao Li, Haobo Wang, Gang Chen, Junbo Zhao

The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size.

Inference Attack Membership Inference Attack

NeurDB: An AI-powered Autonomous Data System

no code implementations7 May 2024 Beng Chin Ooi, Shaofeng Cai, Gang Chen, Kian Lee Tan, Yuncheng Wu, Xiaokui Xiao, Naili Xing, Cong Yue, Lingze Zeng, Meihui Zhang, Zhanhao Zhao

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems.

Powering In-Database Dynamic Model Slicing for Structured Data Analytics

no code implementations1 May 2024 Lingze Zeng, Naili Xing, Shaofeng Cai, Gang Chen, Beng Chin Ooi, Jian Pei, Yuncheng Wu

This SQL-aware MoE technique scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating only necessary experts via the gating network during inference.

Pre-Trained Model Recommendation for Downstream Fine-tuning

no code implementations11 Mar 2024 Jiameng Bai, Sai Wu, Jie Song, Junbo Zhao, Gang Chen

As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task.

Inductive Bias Model Selection +1

FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning

no code implementations7 Mar 2024 Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu

Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy.

Federated Learning

Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA

no code implementations20 Jan 2024 Xi Chen, MingKe You, Li Wang, Weizhi Liu, Yu Fu, Jie Xu, Shaoting Zhang, Gang Chen, Kang Li, Jian Li

This study focused on evaluating and enhancing the clinical capabilities of LLMs in specific domains, using osteoarthritis (OA) management as a case study.

Management Retrieval

Positive-Unlabeled Learning by Latent Group-Aware Meta Disambiguation

1 code implementation CVPR 2024 Lin Long, Haobo Wang, Zhijie Jiang, Lei Feng, Chang Yao, Gang Chen, Junbo Zhao

To cope with this problem we propose a novel PU learning framework namely Latent Group-Aware Meta Disambiguation (LaGAM) which incorporates a hierarchical contrastive learning module to extract the underlying grouping semantics within PU data and produce compact representations.

Binary Classification Contrastive Learning +1

Targeted Representation Alignment for Open-World Semi-Supervised Learning

1 code implementation CVPR 2024 Ruixuan Xiao, Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang

Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen.

Maximum Separation Open-World Semi-Supervised Learning

Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction

no code implementations30 Dec 2023 Shuo Xu, Yucheng Zhang, Gang Chen, Xincheng Xiang, Peng Cong, Yuewen Sun

In this study, we propose a fully unsupervised framework called Deep Radon Prior (DRP), inspired by Deep Image Prior (DIP), to address the aforementioned limitations.

Computed Tomography (CT)

CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition

1 code implementation15 Dec 2023 Cheng Peng, Ke Chen, Lidan Shou, Gang Chen

The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data.

Emotion Recognition Specificity

FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

1 code implementation27 Nov 2023 Ruixuan Xiao, Yiwen Dong, Junbo Zhao, Runze Wu, Minmin Lin, Gang Chen, Haobo Wang

While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention.

Active Learning In-Context Learning

TLM: Token-Level Masking for Transformers

1 code implementation28 Oct 2023 Yangjun Wu, Kebin Fang, Dongxiang Zhang, Han Wang, Hao Zhang, Gang Chen

Structured dropout approaches, such as attention dropout and DropHead, have been investigated to regularize the multi-head attention mechanism in Transformers.

Data-to-Text Generation Grammatical Error Correction +1

Learning to Optimise Climate Sensor Placement using a Transformer

no code implementations18 Oct 2023 Chen Wang, Victoria Huang, Gang Chen, Hui Ma, Bryce Chen, Jochen Schmidt

In this paper, we introduce a novel sensor placement approach focused on learning improvement heuristics using deep reinforcement learning (RL) methods.

Management Reinforcement Learning (RL)

Large-Scale OD Matrix Estimation with A Deep Learning Method

no code implementations9 Oct 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints.

A Simple Text to Video Model via Transformer

1 code implementation26 Sep 2023 Gang Chen

Since both text and video are sequential data, we encode both texts and images into the same hidden space, which are further fed into Transformer to capture the temporal consistency and then decoder to generate either text or images.


ModelGiF: Gradient Fields for Model Functional Distance

1 code implementation ICCV 2023 Jie Song, Zhengqi Xu, Sai Wu, Gang Chen, Mingli Song

The last decade has witnessed the success of deep learning and the surge of publicly released trained models, which necessitates the quantification of the model functional distance for various purposes.

A mixed policy to improve performance of language models on math problems

1 code implementation17 Jul 2023 Gang Chen

Considering math problems are deterministic, we propose a mixed policy exploration approach to solve math problems with reinforcement learning.

GSM8K Math

A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence

no code implementations11 Jul 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization.

Towards Cross-Table Masked Pretraining for Web Data Mining

2 code implementations10 Jul 2023 Chao Ye, Guoshan Lu, Haobo Wang, Liyao Li, Sai Wu, Gang Chen, Junbo Zhao

Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information.

Contrastive Learning

Deep Metric Tensor Regularized Policy Gradient

no code implementations18 May 2023 Gang Chen, Victoria Huang

Armed with these technical developments, we propose a new policy gradient algorithm that learns to minimize the absolute divergence in the Riemannian manifold as an important regularization mechanism, allowing the Riemannian manifold to smoothen its policy gradient vector field.


Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility

1 code implementation15 May 2023 Wentao Ye, Mingfeng Ou, Tianyi Li, Yipeng chen, Xuetao Ma, Yifan Yanggong, Sai Wu, Jie Fu, Gang Chen, Haobo Wang, Junbo Zhao

With most of the related literature in the era of LLM uncharted, we propose an automated workflow that copes with an upscaled number of queries/responses.


Deep Partial Multi-Label Learning with Graph Disambiguation

no code implementations10 May 2023 Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen

In partial multi-label learning (PML), each data example is equipped with a candidate label set, which consists of multiple ground-truth labels and other false-positive labels.

Multi-Label Learning

Controllable Textual Inversion for Personalized Text-to-Image Generation

1 code implementation11 Apr 2023 Jianan Yang, Haobo Wang, YanMing Zhang, Ruixuan Xiao, Sai Wu, Gang Chen, Junbo Zhao

The recent large-scale generative modeling has attained unprecedented performance especially in producing high-fidelity images driven by text prompts.

Active Learning Text-to-Image Generation

Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow

no code implementations24 Mar 2023 Hanyu Zhou, Yi Chang, Gang Chen, Luxin Yan

In motion adaptation, we utilize the flow consistency knowledge to align the cross-domain optical flows into a motion-invariance common space, where the optical flow from clean weather is used as the guidance-knowledge to obtain a preliminary optical flow for adverse weather.

Domain Adaptation Optical Flow Estimation

Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting

1 code implementation13 Feb 2023 Yuchen Liu, Chen Chen, Lingjuan Lyu, Fangzhao Wu, Sai Wu, Gang Chen

In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings.

Federated Learning

FLAC: A Robust Failure-Aware Atomic Commit Protocol for Distributed Transactions

no code implementations9 Feb 2023 Hexiang Pan, Quang-Trung Ta, Meihui Zhang, Yeow Meng Chee, Gang Chen, Beng Chin Ooi

Consequently, it improves both the response time and throughput, and effectively handles nodes distributed across the Internet where crash and network failures might occur.

Speed up the inference of diffusion models via shortcut MCMC sampling

no code implementations18 Dec 2022 Gang Chen

Diffusion probabilistic models have generated high quality image synthesis recently.

Image Generation

Unbiased Knowledge Distillation for Recommendation

1 code implementation27 Nov 2022 Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He

Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.

Knowledge Distillation Model Compression +1

MSRL: Distributed Reinforcement Learning with Dataflow Fragments

no code implementations3 Oct 2022 Huanzhou Zhu, Bo Zhao, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen

Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.

reinforcement-learning Reinforcement Learning (RL)

Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training

no code implementations29 Sep 2022 Gang Chen, Victoria Huang

In this paper, we propose a new technique to train an ensemble of base learners based on an innovative multi-step integration method.

reinforcement-learning Reinforcement Learning (RL)

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning

1 code implementation21 Sep 2022 Haobo Wang, Mingxuan Xia, Yixuan Li, YUREN MAO, Lei Feng, Gang Chen, Junbo Zhao

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth.

Partial Label Learning Weakly-supervised Learning

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

1 code implementation21 Jul 2022 Ruixuan Xiao, Yiwen Dong, Haobo Wang, Lei Feng, Runze Wu, Gang Chen, Junbo Zhao

To overcome the potential side effect of excessive clean set selection procedure, we further devise a novel SSL framework that is able to train balanced and unbiased classifiers on the separated clean and noisy samples.

Learning with noisy labels

Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

no code implementations28 Jun 2022 Haitao Meng, Changcai Li, Gang Chen, Alois Knoll

In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy.

3D Object Detection Object +3

Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network

1 code implementation15 Apr 2022 Gang Chen, Yu Lu, Rong Su, Zhaodan Kong

Machine learning-based methods have achieved successful applications in machinery fault diagnosis.

PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning

1 code implementation22 Jan 2022 Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.

Contrastive Learning Partial Label Learning +2

Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning

no code implementations13 Nov 2021 Gang Chen

For example, we need the Gaussian policy with high variance to explore object of interests in a large image, which may cause randomized search and unstable learning.

Q-Learning Reinforcement Learning (RL)

Contrastive Label Disambiguation for Partial Label Learning

1 code implementation ICLR 2022 Haobo Wang, Ruixuan Xiao, Sharon Li, Lei Feng, Gang Niu, Gang Chen, Junbo Zhao

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.

Contrastive Learning Partial Label Learning +2

Unifying Top-down and Bottom-up for Recurrent Visual Attention

no code implementations29 Sep 2021 Gang Chen

For example, we need the Gaussian policy with high variance to explore object of interests in a large image, which may cause randomized search and unstable learning.


Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal

no code implementations9 Sep 2021 Lei Zhu, Zhaojing Luo, Wei Wang, Meihui Zhang, Gang Chen, Kaiping Zheng

In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models.

Domain Adaptation Transfer Learning

SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

no code implementations3 Aug 2021 Naili Xing, Sai Ho Yeung, ChengHao Cai, Teck Khim Ng, Wei Wang, Kaiyuan Yang, Nan Yang, Meihui Zhang, Gang Chen, Beng Chin Ooi

Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together.

Image Classification

Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable

no code implementations15 Jul 2021 Guanqun Ai, Xingquan Zuo, Gang Chen, Binglin Wu

Building on an existing method for calculating the carrying capacity, we develop a new technique to enhance the matching degree at each bus station.

reinforcement-learning Reinforcement Learning (RL)

ARM-Net: Adaptive Relation Modeling Network for Structured Data

1 code implementation5 Jul 2021 Shaofeng Cai, Kaiping Zheng, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Meihui Zhang

The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.

Attribute Decision Making +1

Joining datasets via data augmentation in the label space for neural networks

no code implementations17 Jun 2021 Jake Zhao, Mingfeng Ou, Linji Xue, Yunkai Cui, Sai Wu, Gang Chen

Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference.

Data Augmentation text-classification +1

A critical look at the current train/test split in machine learning

no code implementations8 Jun 2021 Jimin Tan, Jianan Yang, Sai Wu, Gang Chen, Jake Zhao

The establishment of these split protocols are based on two assumptions: (i)-fixing the dataset to be eternally static so we could evaluate different machine learning algorithms or models; (ii)-there is a complete set of annotated data available to researchers or industrial practitioners.

Active Learning Benchmarking +2

Effective Slot Filling via Weakly-Supervised Dual-Model Learning

1 code implementation AAAI 2021 Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Gang Chen

By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach.

slot-filling Slot Filling +1

Machine vision detection to daily facial fatigue with a nonlocal 3D attention network

no code implementations21 Apr 2021 Zeyu Chen, Xinhang Zhang, Juan Li, Jingxuan Ni, Gang Chen, Shaohua Wang, Fangfang Fan, Changfeng Charles Wang, Xiaotao Li

Our experimental results in multiple metrics proved that our framework captured some typical, micro and dynamic facial features along spatiotemporal dimensions, contributing to the mild fatigue detection in the wild.

Binary Classification

AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment

no code implementations30 Mar 2021 Can Cui, Wei Wang, Meihui Zhang, Gang Chen, Zhaojing Luo, Beng Chin Ooi

In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes.

AutoML Stock Prediction

Thermally Regenerative Flow Batteries with pH Neutral Electrolytes for Harvesting Low-Grade Heat

no code implementations10 Mar 2021 Xin Qian, Jungwoo Shin, Yaodong Tu, James Han Zhang, Gang Chen

This work also presents a comprehensive model with a coupled analysis of mass transfer and reaction kinetics in a porous electrode that can accurately capture the flow rate dependence of power density and energy conversion efficiency.

Applied Physics Classical Physics

Guided Interpolation for Adversarial Training

no code implementations15 Feb 2021 Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama

To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data.

Adversarial Robustness

Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

no code implementations6 Feb 2021 Victoria Huang, Gang Chen, Qiang Fu

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN).

Reinforcement Learning (RL)

Practical development of efficient thermoelectric-photovoltaic hybrid systems based on wide-gap solar cells

no code implementations21 Jan 2021 Bruno Lorenzi, Paolo Mariani, Andrea Reale, Aldo Di Carlo, Gang Chen, Dario Narducci

The model results showed in all three cases efficiency gains with a maximum of +3. 1% for Perovskites (from 16. 4% to 19. 5%).

Applied Physics

Neural Machine Translation: A Review of Methods, Resources, and Tools

no code implementations31 Dec 2020 Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu

Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.

Data Augmentation Machine Translation +2

Towards a Universal Continuous Knowledge Base

no code implementations25 Dec 2020 Gang Chen, Maosong Sun, Yang Liu

In this work, we propose a method for building a continuous knowledge base (CKB) that can store knowledge imported from multiple, diverse neural networks.

Knowledge Distillation text-classification +2

Writing and deleting skyrmions with electric fields in a multiferroic heterostructure

no code implementations16 Dec 2020 Chao-Kai Li, Xu-Ping Yao, Gang Chen

Magnetic skyrmions are topological spin textures that can be used as information carriers for the next-generation information storage and processing.

Strongly Correlated Electrons Mesoscale and Nanoscale Physics Materials Science

Learning Symbolic Expressions via Gumbel-Max Equation Learner Networks

no code implementations12 Dec 2020 Gang Chen

Most of the neural networks (NNs) learned via state-of-the-art machine learning techniques are black-box models.

BIG-bench Machine Learning regression +1

LINDT: Tackling Negative Federated Learning with Local Adaptation

no code implementations23 Nov 2020 Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu

On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model.

Federated Learning

Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet

no code implementations18 Nov 2020 Yayuan Qin, Yao Shen, ChangLe Liu, Hongliang Wo, Yonghao Gao, Yu Feng, Xiaowen Zhang, Gaofeng Ding, Yiqing Gu, Qisi Wang, Shoudong Shen, Helen C. Walker, Robert Bewley, Jianhui Xu, Martin Boehm, Paul Steffens, Seiko Ohira-Kawamura, Naoki Murai, Astrid Schneidewind, Xin Tong, Gang Chen, Jun Zhao

We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields.

Strongly Correlated Electrons Materials Science

BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention

no code implementations9 Nov 2020 Zhebin Zhang, Sai Wu, Dawei Jiang, Gang Chen

In this work, we propose a novel BERT-enhanced NMT model called BERT-JAM which improves upon existing models from two aspects: 1) BERT-JAM uses joint-attention modules to allow the encoder/decoder layers to dynamically allocate attention between different representations, and 2) BERT-JAM allows the encoder/decoder layers to make use of BERT's intermediate representations by composing them using a gated linear unit (GLU).

Decoder Machine Translation +2

MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines

no code implementations17 Oct 2020 Zhaojing Luo, Sai Ho Yeung, Meihui Zhang, Kaiping Zheng, Lei Zhu, Gang Chen, Feiyi Fan, Qian Lin, Kee Yuan Ngiam, Beng Chin Ooi

In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.

BIG-bench Machine Learning Management

Privacy Preserving Vertical Federated Learning for Tree-based Models

no code implementations14 Aug 2020 Yuncheng Wu, Shaofeng Cai, Xiaokui Xiao, Gang Chen, Beng Chin Ooi

Federated learning (FL) is an emerging paradigm that enables multiple organizations to jointly train a model without revealing their private data to each other.

Privacy Preserving Vertical Federated Learning


1 code implementation11 Aug 2020 Gang Chen, Yi Ding, Hugo Edwards, Chong Hin Chau, Sai Hou, Grace Johnson, Mohammed Sharukh Syed, Haoyuan Tang, Yue Wu, Ye Yan, Gil Tidhar, Nir Lipovetzky

Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL.

Higher-order topological semimetal in acoustic crystals

no code implementations8 Jul 2020 Qiang Wei, Xuewei Zhang, Weiyin Deng, Jiuyang Lu, Xueqin Huang, Mou Yan, Gang Chen, Zhengyou Liu, Suotang Jia

Here we report the realization of a second-order topological Weyl semimetal in a 3D-printed acoustic crystal, which possesses Weyl points in 3D momentum space, 2D Fermi arc states on surfaces and 1D gapless states on hinges.

Mesoscale and Nanoscale Physics

Pyramid: A Layered Model for Nested Named Entity Recognition

2 code implementations ACL 2020 Jue Wang, Lidan Shou, Ke Chen, Gang Chen

Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.

named-entity-recognition Named Entity Recognition +2

Robust Federated Recommendation System

no code implementations15 Jun 2020 Chen Chen, Jingfeng Zhang, Anthony K. H. Tung, Mohan Kankanhalli, Gang Chen

We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients.

Recommendation Systems

Decorrelated Double Q-learning

no code implementations12 Jun 2020 Gang Chen

Q-learning with value function approximation may have the poor performance because of overestimation bias and imprecise estimate.

Continuous Control Q-Learning +2

A Transactional Perspective on Execute-order-validate Blockchains

2 code implementations23 Mar 2020 Pingcheng Ruan, Dumitrel Loghin, Quang-Trung Ta, Meihui Zhang, Gang Chen, Beng Chin Ooi

For evaluation, we implement our method in two blockchains respectively, FabricSharp on top of Hyperledger Fabric, and FastFabricSharp on top of FastFabric.

Distributed, Parallel, and Cluster Computing Databases Performance

Analysis of Indexing Structures for Immutable Data

2 code implementations4 Mar 2020 Cong Yue, Zhongle Xie, Meihui Zhang, Gang Chen, Beng Chin Ooi, Sheng Wang, Xiaokui Xiao

We establish the worst-case guarantees of each index in terms of these five metrics, and we experimentally evaluate all indexes in a large variety of settings.


The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

ISBNet: Instance-aware Selective Branching Networks

no code implementations ICLR 2020 Shaofeng Cai, Yao Shu, Wei Wang, Gang Chen, Beng Chin Ooi

Recent years have witnessed growing interests in designing efficient neural networks and neural architecture search (NAS).

Neural Architecture Search

Learning to Predict Explainable Plots for Neural Story Generation

no code implementations5 Dec 2019 Gang Chen, Yang Liu, Huanbo Luan, Meng Zhang, Qun Liu, Maosong Sun

While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge.

Sentence Story Generation

PhoneBit: Efficient GPU-Accelerated Binary Neural Network Inference Engine for Mobile Phones

no code implementations5 Dec 2019 Gang Chen, Shengyu He, Haitao Meng, Kai Huang

Over the last years, a great success of deep neural networks (DNNs) has been witnessed in computer vision and other fields.

Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning

no code implementations24 Nov 2019 Gang Chen

Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs).

Context-aware Active Multi-Step Reinforcement Learning

no code implementations11 Nov 2019 Gang Chen, Dingcheng Li, ran Xu

Then given the selected samples, we propose the adaptive multi-step TD, which generalizes TD($\lambda$), but adaptively switch on/off the backups from future returns of different steps.

Active Learning Decision Making +2

Stain Style Transfer using Transitive Adversarial Networks

no code implementations23 Oct 2019 Shaojin Cai, Yuyang Xue3 Qinquan Gao, Min Du, Gang Chen, Hejun Zhang, Tong Tong

It is not necessary for an expert to pick a representative reference slide in the proposed TAN method.

Style Transfer

A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation

no code implementations21 Oct 2019 Gang Chen

Guided by this framework and the maximum-entropy learning technique, we will first train agents' policies with shared global component to foster coordinated and effective learning.

Multi-agent Reinforcement Learning

Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding

no code implementations13 Oct 2019 Gang Chen, Hongzhe Yu, Wei Dong, Xinjun Sheng, Xiangyang Zhu, Han Ding

While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage.


Blockchains vs. Distributed Databases: Dichotomy and Fusion

1 code implementation3 Oct 2019 Pingcheng Ruan, Gang Chen, Tien Tuan Anh Dinh, Qian Lin, Dumitrel Loghin, Beng Chin Ooi, Meihui Zhang

As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems.

Databases Performance

The Disruptions of 5G on Data-driven Technologies and Applications

no code implementations6 Sep 2019 Dumitrel Loghin, Shaofeng Cai, Gang Chen, Tien Tuan Anh Dinh, Feiyi Fan, Qian Lin, Janice Ng, Beng Chin Ooi, Xutao Sun, Quang-Trung Ta, Wei Wang, Xiaokui Xiao, Yang Yang, Meihui Zhang, Zhonghua Zhang

With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management.

Networking and Internet Architecture Databases Distributed, Parallel, and Cluster Computing

Database Meets Deep Learning: Challenges and Opportunities

no code implementations21 Jun 2019 Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Beng Chin Ooi, Kian-Lee Tan

Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition.

Image Classification speech-recognition +1

Memetic EDA-Based Approaches to Comprehensive Quality-Aware Automated Semantic Web Service Composition

no code implementations19 Jun 2019 Chen Wang, Hui Ma, Gang Chen, Sven Hartmann

The objective of this problem is to find a solution with optimized or near-optimized overall QoS and QoSM within polynomial time over a service request.

Service Composition

Blockchain Goes Green? An Analysis of Blockchain on Low-Power Nodes

2 code implementations16 May 2019 Dumitrel Loghin, Gang Chen, Tien Tuan Anh Dinh, Beng Chin Ooi, Yong Meng Teo

Motivated by the massive energy usage of blockchain, on the one hand, and by significant performance improvements in low-power, wimpy systems, on the other hand, we perform an in-depth time-energy analysis of blockchain systems on low-power nodes in comparison to high-performance nodes.

Distributed, Parallel, and Cluster Computing Databases Emerging Technologies Performance

Effective and Efficient Dropout for Deep Convolutional Neural Networks

no code implementations6 Apr 2019 Shaofeng Cai, Yao Shu, Gang Chen, Beng Chin Ooi, Wei Wang, Meihui Zhang

However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs.


Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints

1 code implementation3 Apr 2019 Shaofeng Cai, Gang Chen, Beng Chin Ooi, Jinyang Gao

Model slicing could be viewed as an elastic computation solution without requiring more computational resources.

Model Compression

Evolutionary Multitasking for Semantic Web Service Composition

no code implementations18 Feb 2019 Chen Wang, Hui Ma, Gang Chen, Sven Hartmann

We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

Service Composition

Off-Policy Actor-Critic in an Ensemble: Achieving Maximum General Entropy and Effective Environment Exploration in Deep Reinforcement Learning

no code implementations14 Feb 2019 Gang Chen, Yiming Peng

We propose a new policy iteration theory as an important extension of soft policy iteration and Soft Actor-Critic (SAC), one of the most efficient model free algorithms for deep reinforcement learning.

Optimizing Controller Placement for Software-Defined Networks

no code implementations14 Feb 2019 Victoria Huang, Gang Chen, Qiang Fu, Elliott Wen

In comparison to communication delay, existing literature on the CPP assumes that the influence of controller workload distribution on network performance is negligible.

Composing Distributed Data-intensive Web Services Using a Flexible Memetic Algorithm

no code implementations26 Jan 2019 Soheila Sadeghiram, Hui Ma, Gang Chen

Data-intensive Web services, which manipulate and deal with those data, are of great interest to implement data-intensive processes, such as distributed Data-intensive Web Service Composition (DWSC).

Service Composition

Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition

no code implementations16 Jan 2019 Soheila Sadeghiram, Hui Ma, Gang Chen

As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously.

Distributed Computing Service Composition

Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization

no code implementations2 Sep 2018 Gang Chen, Yiming Peng, Mengjie Zhang

With the aim of improving sample efficiency and learning performance, we will develop a new DRL algorithm in this paper that seamless integrates entropy-induced and bootstrap-induced techniques for efficient and deep exploration of the learning environment.

Reinforcement Learning (RL)

PANDA: Facilitating Usable AI Development

no code implementations26 Apr 2018 Jinyang Gao, Wei Wang, Meihui Zhang, Gang Chen, H. V. Jagadish, Guoliang Li, Teck Khim Ng, Beng Chin Ooi, Sheng Wang, Jingren Zhou

In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.

Autonomous Driving

Rafiki: Machine Learning as an Analytics Service System

1 code implementation PVLDB (The Proceedings of the VLDB Endowment) 2018 Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, Beng Chin Ooi

Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users.

BIG-bench Machine Learning Hyperparameter Optimization +2

An Adaptive Clipping Approach for Proximal Policy Optimization

no code implementations17 Apr 2018 Gang Chen, Yiming Peng, Mengjie Zhang

While PPO is inspired by the same learning theory that justifies trust region policy optimization (TRPO), PPO substantially simplifies algorithm design and improves data efficiency by performing multiple epochs of \emph{clipped policy optimization} from sampled data.

Learning Theory

ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications

no code implementations14 Feb 2018 Sheng Wang, Tien Tuan Anh Dinh, Qian Lin, Zhongle Xie, Meihui Zhang, Qingchao Cai, Gang Chen, Wanzeng Fu, Beng Chin Ooi, Pingcheng Ruan

By integrating the core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort.

Databases Cryptography and Security Distributed, Parallel, and Cluster Computing

A Roadmap for HEP Software and Computing R&D for the 2020s

1 code implementation18 Dec 2017 Johannes Albrecht, Antonio Augusto Alves Jr, Guilherme Amadio, Giuseppe Andronico, Nguyen Anh-Ky, Laurent Aphecetche, John Apostolakis, Makoto Asai, Luca Atzori, Marian Babik, Giuseppe Bagliesi, Marilena Bandieramonte, Sunanda Banerjee, Martin Barisits, Lothar A. T. Bauerdick, Stefano Belforte, Douglas Benjamin, Catrin Bernius, Wahid Bhimji, Riccardo Maria Bianchi, Ian Bird, Catherine Biscarat, Jakob Blomer, Kenneth Bloom, Tommaso Boccali, Brian Bockelman, Tomasz Bold, Daniele Bonacorsi, Antonio Boveia, Concezio Bozzi, Marko Bracko, David Britton, Andy Buckley, Predrag Buncic, Paolo Calafiura, Simone Campana, Philippe Canal, Luca Canali, Gianpaolo Carlino, Nuno Castro, Marco Cattaneo, Gianluca Cerminara, Javier Cervantes Villanueva, Philip Chang, John Chapman, Gang Chen, Taylor Childers, Peter Clarke, Marco Clemencic, Eric Cogneras, Jeremy Coles, Ian Collier, David Colling, Gloria Corti, Gabriele Cosmo, Davide Costanzo, Ben Couturier, Kyle Cranmer, Jack Cranshaw, Leonardo Cristella, David Crooks, Sabine Crépé-Renaudin, Robert Currie, Sünje Dallmeier-Tiessen, Kaushik De, Michel De Cian, Albert De Roeck, Antonio Delgado Peris, Frédéric Derue, Alessandro Di Girolamo, Salvatore Di Guida, Gancho Dimitrov, Caterina Doglioni, Andrea Dotti, Dirk Duellmann, Laurent Duflot, Dave Dykstra, Katarzyna Dziedziniewicz-Wojcik, Agnieszka Dziurda, Ulrik Egede, Peter Elmer, Johannes Elmsheuser, V. Daniel Elvira, Giulio Eulisse, Steven Farrell, Torben Ferber, Andrej Filipcic, Ian Fisk, Conor Fitzpatrick, José Flix, Andrea Formica, Alessandra Forti, Giovanni Franzoni, James Frost, Stu Fuess, Frank Gaede, Gerardo Ganis, Robert Gardner, Vincent Garonne, Andreas Gellrich, Krzysztof Genser, Simon George, Frank Geurts, Andrei Gheata, Mihaela Gheata, Francesco Giacomini, Stefano Giagu, Manuel Giffels, Douglas Gingrich, Maria Girone, Vladimir V. Gligorov, Ivan Glushkov, Wesley Gohn, Jose Benito Gonzalez Lopez, Isidro González Caballero, Juan R. González Fernández, Giacomo Govi, Claudio Grandi, Hadrien Grasland, Heather Gray, Lucia Grillo, Wen Guan, Oliver Gutsche, Vardan Gyurjyan, Andrew Hanushevsky, Farah Hariri, Thomas Hartmann, John Harvey, Thomas Hauth, Benedikt Hegner, Beate Heinemann, Lukas Heinrich, Andreas Heiss, José M. Hernández, Michael Hildreth, Mark Hodgkinson, Stefan Hoeche, Burt Holzman, Peter Hristov, Xingtao Huang, Vladimir N. Ivanchenko, Todor Ivanov, Jan Iven, Brij Jashal, Bodhitha Jayatilaka, Roger Jones, Michel Jouvin, Soon Yung Jun, Michael Kagan, Charles William Kalderon, Meghan Kane, Edward Karavakis, Daniel S. Katz, Dorian Kcira, Oliver Keeble, Borut Paul Kersevan, Michael Kirby, Alexei Klimentov, Markus Klute, Ilya Komarov, Dmitri Konstantinov, Patrick Koppenburg, Jim Kowalkowski, Luke Kreczko, Thomas Kuhr, Robert Kutschke, Valentin Kuznetsov, Walter Lampl, Eric Lancon, David Lange, Mario Lassnig, Paul Laycock, Charles Leggett, James Letts, Birgit Lewendel, Teng Li, Guilherme Lima, Jacob Linacre, Tomas Linden, Miron Livny, Giuseppe Lo Presti, Sebastian Lopienski, Peter Love, Adam Lyon, Nicolò Magini, Zachary L. Marshall, Edoardo Martelli, Stewart Martin-Haugh, Pere Mato, Kajari Mazumdar, Thomas McCauley, Josh McFayden, Shawn McKee, Andrew McNab, Rashid Mehdiyev, Helge Meinhard, Dario Menasce, Patricia Mendez Lorenzo, Alaettin Serhan Mete, Michele Michelotto, Jovan Mitrevski, Lorenzo Moneta, Ben Morgan, Richard Mount, Edward Moyse, Sean Murray, Armin Nairz, Mark S. Neubauer, Andrew Norman, Sérgio Novaes, Mihaly Novak, Arantza Oyanguren, Nurcan Ozturk, Andres Pacheco Pages, Michela Paganini, Jerome Pansanel, Vincent R. Pascuzzi, Glenn Patrick, Alex Pearce, Ben Pearson, Kevin Pedro, Gabriel Perdue, Antonio Perez-Calero Yzquierdo, Luca Perrozzi, Troels Petersen, Marko Petric, Andreas Petzold, Jónatan Piedra, Leo Piilonen, Danilo Piparo, Jim Pivarski, Witold Pokorski, Francesco Polci, Karolos Potamianos, Fernanda Psihas, Albert Puig Navarro, Günter Quast, Gerhard Raven, Jürgen Reuter, Alberto Ribon, Lorenzo Rinaldi, Martin Ritter, James Robinson, Eduardo Rodrigues, Stefan Roiser, David Rousseau, Gareth Roy, Grigori Rybkine, Andre Sailer, Tai Sakuma, Renato Santana, Andrea Sartirana, Heidi Schellman, Jaroslava Schovancová, Steven Schramm, Markus Schulz, Andrea Sciabà, Sally Seidel, Sezen Sekmen, Cedric Serfon, Horst Severini, Elizabeth Sexton-Kennedy, Michael Seymour, Davide Sgalaberna, Illya Shapoval, Jamie Shiers, Jing-Ge Shiu, Hannah Short, Gian Piero Siroli, Sam Skipsey, Tim Smith, Scott Snyder, Michael D. Sokoloff, Panagiotis Spentzouris, Hartmut Stadie, Giordon Stark, Gordon Stewart, Graeme A. Stewart, Arturo Sánchez, Alberto Sánchez-Hernández, Anyes Taffard, Umberto Tamponi, Jeff Templon, Giacomo Tenaglia, Vakhtang Tsulaia, Christopher Tunnell, Eric Vaandering, Andrea Valassi, Sofia Vallecorsa, Liviu Valsan, Peter Van Gemmeren, Renaud Vernet, Brett Viren, Jean-Roch Vlimant, Christian Voss, Margaret Votava, Carl Vuosalo, Carlos Vázquez Sierra, Romain Wartel, Gordon T. Watts, Torre Wenaus, Sandro Wenzel, Mike Williams, Frank Winklmeier, Christoph Wissing, Frank Wuerthwein, Benjamin Wynne, Zhang Xiaomei, Wei Yang, Efe Yazgan

Particle physics has an ambitious and broad experimental programme for the coming decades.

Computational Physics High Energy Physics - Experiment

Untangling Blockchain: A Data Processing View of Blockchain Systems

1 code implementation17 Aug 2017 Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang

Blockchain technologies are gaining massive momentum in the last few years.

Databases Cryptography and Security

BLOCKBENCH: A Framework for Analyzing Private Blockchains

2 code implementations12 Mar 2017 Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan

However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other.

Databases Cryptography and Security Distributed, Parallel, and Cluster Computing

Joint Visual Denoising and Classification using Deep Learning

1 code implementation4 Dec 2016 Gang Chen, Yawei Li, Sargur N. Srihari

Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model.

Classification Denoising +1

Word Recognition with Deep Conditional Random Fields

1 code implementation4 Dec 2016 Gang Chen, Yawei Li, Sargur N. Srihari

On the other hand, word recognition is a sequential problem where we need to model the correlation between characters.

Scene Recognition

A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation

1 code implementation8 Oct 2016 Gang Chen

We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text.

Handwriting Recognition speech-recognition +1

Deep Learning At Scale and At Ease

no code implementations25 Mar 2016 Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

The other is scalability, that is the deep learning system must be able to provision for a huge demand of computing resources for training large models with massive datasets.

Image Classification

A Light Sliding-Window Part-of-Speech Tagger for the Apertium Free/Open-Source Machine Translation Platform

no code implementations18 Sep 2015 Gang Chen, Mikel L. Forcada

This paper describes a free/open-source implementation of the light sliding-window (LSW) part-of-speech tagger for the Apertium free/open-source machine translation platform.

Machine Translation Translation

Generalized K-fan Multimodal Deep Model with Shared Representations

no code implementations26 Mar 2015 Gang Chen, Sargur N. Srihari

In this paper, we propose a K-fan deep structure model, which can handle the multi-input and muti-output learning problems effectively.

Information Retrieval Object Recognition +1

Deep Transductive Semi-supervised Maximum Margin Clustering

no code implementations26 Jan 2015 Gang Chen

Thus, our model unifies transductive learning, feature learning and maximum margin techniques in the semi-supervised clustering framework.

Clustering Transductive Learning

Deep Learning with Nonparametric Clustering

1 code implementation13 Jan 2015 Gang Chen

As an unsupervised method, our model first leverages the advantages of deep learning for feature representation and dimension reduction.

Clustering Dimensionality Reduction +2

Sequential Labeling with online Deep Learning

no code implementations10 Dec 2014 Gang Chen, ran Xu, Sargur Srihari

Deep learning has attracted great attention recently and yielded the state of the art performance in dimension reduction and classification problems.

Dimensionality Reduction

Compositional Structure Learning for Action Understanding

no code implementations21 Oct 2014 Ran Xu, Gang Chen, Caiming Xiong, Wei Chen, Jason J. Corso

The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile robotics and video search, with solutions to classification, localization and detection.

Action Detection Action Understanding +1

Restricted Boltzmann Machine for Classification with Hierarchical Correlated Prior

no code implementations13 Jun 2014 Gang Chen, Sargur H. Srihari

We propose a hierarchical correlated RBM for classification problem, which generalizes the classification RBM with sharing information among different classes.

Classification General Classification

Latent Fisher Discriminant Analysis

no code implementations21 Sep 2013 Gang Chen

In this paper, we overcome this limitation and propose a latent variable Fisher discriminant analysis model.

Dimensionality Reduction General Classification +2

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