Search Results for author: Tao Huang

Found 24 papers, 7 papers with code

Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy

no code implementations Findings (EMNLP) 2021 Tao Huang, Hong Chen

To improve the privacy guarantee and efficiency, we combine a subsampling method with CGS and propose a novel LDA training algorithm with differential privacy, SUB-LDA.

Knowledge Distillation from A Stronger Teacher

1 code implementation21 May 2022 Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu

In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly.

Knowledge Distillation Object Detection +1

Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation

no code implementations13 May 2022 Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn, Adeel Razi, Wei Xiang

Therefore, most of the previous studies proposed SR reconstruction by employing authentic HR images and synthetic LR images downsampled from the HR images, yet the difference in degradation representations between synthetic and authentic LR images suppresses the performance of SR reconstruction from authentic LR images.

Image Registration Representation Learning +1

DyRep: Bootstrapping Training with Dynamic Re-parameterization

1 code implementation24 Mar 2022 Tao Huang, Shan You, Bohan Zhang, Yuxuan Du, Fei Wang, Chen Qian, Chang Xu

Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.

Relational Surrogate Loss Learning

1 code implementation ICLR 2022 Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu

Evaluation metrics in machine learning are often hardly taken as loss functions, as they could be non-differentiable and non-decomposable, e. g., average precision and F1 score.

Image Classification Machine Reading Comprehension +2

Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning

no code implementations18 Jan 2022 Tao Huang, Jiachen Wang, Xiao Chen

Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL).

Data Augmentation reinforcement-learning +1

GreedyNASv2: Greedier Search with a Greedy Path Filter

no code implementations24 Nov 2021 Tao Huang, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Xiaogang Wang, Chang Xu

In this paper, we leverage an explicit path filter to capture the characteristics of paths and directly filter those weak ones, so that the search can be thus implemented on the shrunk space more greedily and efficiently.

Deep Instance Segmentation with Automotive Radar Detection Points

no code implementations5 Oct 2021 Jianan Liu, Weiyi Xiong, Liping Bai, Yuxuan Xia, Tao Huang, Wanli Ouyang, Bing Zhu

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points.

Autonomous Driving Instance Segmentation +1

Reinforcement Learning with Predictive Consistent Representations

no code implementations29 Sep 2021 Tao Huang, Xiao Chen, Jiachen Wang

Learning informative representations from image-based observations is a fundamental problem in deep Reinforcement Learning (RL).


Gradient Boosted Binary Histogram Ensemble for Large-scale Regression

no code implementations3 Jun 2021 Hanyuan Hang, Tao Huang, Yuchao Cai, Hanfang Yang, Zhouchen Lin

In this paper, we propose a gradient boosting algorithm for large-scale regression problems called \textit{Gradient Boosted Binary Histogram Ensemble} (GBBHE) based on binary histogram partition and ensemble learning.

Ensemble Learning

Prioritized Architecture Sampling with Monto-Carlo Tree Search

1 code implementation CVPR 2021 Xiu Su, Tao Huang, Yanxi Li, Shan You, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once.

Neural Architecture Search

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

1 code implementation CVPR 2021 Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi

Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).

Locally Free Weight Sharing for Network Width Search

no code implementations ICLR 2021 Xiu Su, Shan You, Tao Huang, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.

Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

5 code implementations CVPR 2021 Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, Jianzhuang Liu

In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.

Image Denoising Self-Supervised Learning

Wasserstein Distributionally Robust Optimization: A Three-Player Game Framework

no code implementations1 Jan 2021 Zhuozhuo Tu, Shan You, Tao Huang, DaCheng Tao

Wasserstein distributionally robust optimization (DRO) has recently received significant attention in machine learning due to its connection to generalization, robustness and regularization.

Explicit Learning Topology for Differentiable Neural Architecture Search

no code implementations1 Jan 2021 Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang

Differentiable neural architecture search (NAS) has gained much success in discovering more flexible and diverse cell types.

Neural Architecture Search

Stretchable Cells Help DARTS Search Better

no code implementations18 Nov 2020 Tao Huang, Shan You, Yibo Yang, Zhuozhuo Tu, Fei Wang, Chen Qian, ChangShui Zhang

However, even for this consistent search, the searched cells often suffer from poor performance, especially for the supernet with fewer layers, as current DARTS methods are prone to wide and shallow cells, and this topology collapse induces sub-optimal searched cells.

Neural Architecture Search

MG-GCN: Fast and Effective Learning with Mix-grained Aggregators for Training Large Graph Convolutional Networks

no code implementations17 Nov 2020 Tao Huang, Yihan Zhang, Jiajing Wu, Junyuan Fang, Zibin Zheng

To tackle the dilemma between accuracy and efficiency, we propose to use aggregators with different granularities to gather neighborhood information in different layers.

Data Agnostic Filter Gating for Efficient Deep Networks

no code implementations28 Oct 2020 Xiu Su, Shan You, Tao Huang, Hongyan Xu, Fei Wang, Chen Qian, ChangShui Zhang, Chang Xu

To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e. g., FLOPs).

Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers

1 code implementation20 Oct 2020 Yuxuan Du, Tao Huang, Shan You, Min-Hsiu Hsieh, DaCheng Tao

Quantum error mitigation techniques are at the heart of quantum hardware implementation, and are the key to performance improvement of the variational quantum learning scheme (VQLS).

MMD GAN with Random-Forest Kernels

no code implementations ICLR 2020 Tao Huang, Zhen Han, Xu Jia, Hanyuan Hang

In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performance of MMD GAN.

Ensemble Learning

Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments

no code implementations20 Dec 2019 Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui Li

To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).

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