Search Results for author: Wei Dai

Found 43 papers, 11 papers with code

Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

no code implementations26 May 2022 Wei Dai, Chuanfeng Ning, Shiyu Pei, Song Zhu, Xuesong Wang

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning.

HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis

2 code implementations9 May 2022 Wei Dai, Rui Liu, Tianyi Wu, Min Wang, Jianqin Yin, Jun Liu

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin conditions and disorders.

Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain

no code implementations20 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.

A New Learning Paradigm for Stochastic Configuration Network: SCN+

no code implementations11 Mar 2022 Yanshuang Ao, Xinyu Zhou, Wei Dai

This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN.

Incremental Learning

Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation

no code implementations2 Mar 2022 Wei Dai, Daniel Berleant

We created comprehensive 69 benchmarking image sets, including a clean set, sets with single factor perturbations, and sets with two-factor perturbation conditions.

Dictionary Learning Using Rank-One Atomic Decomposition (ROAD)

no code implementations25 Oct 2021 Cheng Cheng, Wei Dai

Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented.

Dictionary Learning

Dictionary Learning with Convex Update (ROMD)

no code implementations13 Oct 2021 Cheng Cheng, Wei Dai

Typical methods for dictionary update focuses on refining both dictionary atoms and their corresponding sparse coefficients by using the sparsity patterns obtained from sparse coding stage, and hence it is a non-convex bilinear inverse problem.

Dictionary Learning

Short-and-Sparse Deconvolution Via Rank-One Constrained Optimization (ROCO)

no code implementations5 Oct 2021 Cheng Cheng, Wei Dai

In the literature, formulations of blind deconvolution is either a convex programming via a matrix lifting of convolution, or a bilinear Lasso.

CAN3D: Fast 3D Medical Image Segmentation via Compact Context Aggregation

no code implementations12 Sep 2021 Wei Dai, Boyeong Woo, Siyu Liu, Matthew Marques, Craig B. Engstrom, Peter B. Greer, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra

Direct automatic segmentation of objects from 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying a number of individual objects with complex geometries within a large volume under investigation.

Medical Image Segmentation Semantic Segmentation

Conflict-Free Four-Dimensional Path Planning for Urban Air Mobility Considering Airspace Occupations

no code implementations27 Jul 2021 Wei Dai, Bizhao Pang, Kin Huat Low

This paper aims at tackling conflict-free path planning problem for UAM operation with a consideration of four-dimensional airspace management.

Price change prediction of ultra high frequency financial data based on temporal convolutional network

no code implementations1 Jul 2021 Wei Dai, Yuan An, Wen Long

Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper.

Deep Kernel Gaussian Process Based Financial Market Predictions

no code implementations26 May 2021 Yong Shi, Wei Dai, Wen Long, Bo Li

However, the deep kernel Gaussian Process has not been applied to forecast the conditional returns and volatility in financial market to the best of our knowledge.

VMAF And Variants: Towards A Unified VQA

no code implementations13 Mar 2021 Pankaj Topiwala, Wei Dai, Jiangfeng Pian, Katalina Biondi, Arvind Krovvidi

We investigate variants of the popular VMAF video quality assessment algorithm for the FR case, using both support vector regression and feedforward neural networks.

feature selection Video Quality Assessment +1

Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation

2 code implementations2 Mar 2021 Wei Dai, Daniel Berleant

Also, we introduce a new four-quadrant statistical visualization tool, including minimum accuracy, maximum accuracy, mean accuracy, and coefficient of variation, for benchmarking robustness of DL classifiers.

Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

no code implementations7 Jan 2021 Yong Shi, Wei Dai, Wen Long, Bo Li

In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.

Where is the Model Looking At?--Concentrate and Explain the Network Attention

no code implementations29 Sep 2020 Wenjia Xu, Jiuniu Wang, Yang Wang, Guangluan Xu, Wei Dai, Yirong Wu

We generate attribute-based textual explanations for the network and ground the attributes on the image to show visual explanations.

Image Classification Multi-Task Learning

Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

1 code implementation28 Jun 2020 Siyu Liu, Wei Dai, Craig Engstrom, Jurgen Fripp, Peter B. Greer, Stuart Crozier, Jason A. Dowling, Shekhar S. Chandra

In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner.

Semantic Segmentation Transfer Learning

Learning Optimal Tree Models Under Beam Search

1 code implementation ICML 2020 Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.

Information Retrieval Recommendation Systems

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

2 code implementations27 Dec 2019 Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai, Kim Laine, Madanlal Musuvathi

We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

HEAX: An Architecture for Computing on Encrypted Data

no code implementations20 Sep 2019 M. Sadegh Riazi, Kim Laine, Blake Pelton, Wei Dai

Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data.

Charge-Based Prison Term Prediction with Deep Gating Network

no code implementations IJCNLP 2019 Huajie Chen, Deng Cai, Wei Dai, Zehui Dai, Yadong Ding

Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction.

feature selection

Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics

1 code implementation5 Jul 2019 Wei Dai, Daniel Berleant

This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks.

Dictionary Learning with BLOTLESS Update

1 code implementation24 Jun 2019 Qi Yu, Wei Dai, Zoran Cvetkovic, Jubo Zhu

BLOTLESS updates a block of dictionary elements and the corresponding sparse coefficients simultaneously.

Dictionary Learning

Improving Data Quality through Deep Learning and Statistical Models

no code implementations16 Oct 2018 Wei Dai, Kenji Yoshigoe, William Parsley

Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy.

Toward Understanding the Impact of Staleness in Distributed Machine Learning

no code implementations ICLR 2019 Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing

Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates.

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

no code implementations29 Jul 2018 Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing

Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.

whole slide images

Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks

no code implementations11 Dec 2017 Hao Zhang, Shizhen Xu, Graham Neubig, Wei Dai, Qirong Ho, Guangwen Yang, Eric P. Xing

Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences of variable lengths, trees, and graphs.

graph construction

Dual Motion GAN for Future-Flow Embedded Video Prediction

no code implementations ICCV 2017 Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing

To make both synthesized future frames and flows indistinguishable from reality, a dual adversarial training method is proposed to ensure that the future-flow prediction is able to help infer realistic future-frames, while the future-frame prediction in turn leads to realistic optical flows.

Representation Learning Video Prediction

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

no code implementations11 Jun 2017 Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing

We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.

Image Classification

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

no code implementations26 Mar 2017 Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing

Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.

A Comparison of deep learning methods for environmental sound

1 code implementation20 Mar 2017 Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das

On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.

Learning Filter Banks Using Deep Learning For Acoustic Signals

no code implementations29 Nov 2016 Shuhui Qu, Juncheng Li, Wei Dai, Samarjit Das

Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.

Very Deep Convolutional Neural Networks for Raw Waveforms

8 code implementations1 Oct 2016 Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, Samarjit Das

Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.

Representation Learning

Strategies and Principles of Distributed Machine Learning on Big Data

no code implementations31 Dec 2015 Eric P. Xing, Qirong Ho, Pengtao Xie, Wei Dai

Taking the view that Big ML systems can benefit greatly from ML-rooted statistical and algorithmic insights --- and that ML researchers should therefore not shy away from such systems design --- we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions.

LightLDA: Big Topic Models on Modest Compute Clusters

1 code implementation4 Dec 2014 Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma

When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.

Topic Models

High-Performance Distributed ML at Scale through Parameter Server Consistency Models

no code implementations29 Oct 2014 Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth Gibson, Eric P. Xing

As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands.

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

no code implementations22 Sep 2014 Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing

We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework.

Petuum: A New Platform for Distributed Machine Learning on Big Data

no code implementations30 Dec 2013 Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yao-Liang Yu

What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?

Consistent Bounded-Asynchronous Parameter Servers for Distributed ML

no code implementations30 Dec 2013 Jinliang Wei, Wei Dai, Abhimanu Kumar, Xun Zheng, Qirong Ho, Eric P. Xing

Many ML algorithms fall into the category of \emph{iterative convergent algorithms} which start from a randomly chosen initial point and converge to optima by repeating iteratively a set of procedures.

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